Research Article Histogram of Maximal Optical Flow Projection for Abnormal …downloads.hindawi.com/journals/ijdsn/2015/406941.pdf · 2015-11-24 · Research Article Histogram of

Research ArticleHistogram of Maximal Optical Flow Projection for AbnormalEvents Detection in Crowded Scenes

Ang Li1 Zhenjiang Miao1 Yigang Cen1 Tian Wang2 and Viacheslav Voronin3

1 Institute of Information Science Beijing Jiaotong University Beijing 100044 China2School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China3Department of Radio-Electronic Systems Don State Technical University Shakhty 346500 Russia

Correspondence should be addressed to Ang Li lianghit126com

Received 21 May 2015 Revised 29 July 2015 Accepted 11 August 2015

Academic Editor Shaojie Tang

Copyright copy 2015 Ang Li et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Abnormal events detection plays an important role in the video surveillance which is a challenging subject in the intelligentdetection In this paper based on a novel motion feature descriptor that is the histogram of maximal optical flow projection(HMOFP) we propose an algorithm to detect abnormal events in crowded scenes Following the extraction of the HMOFP ofthe training frames the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of thetesting frames Compared with other methods based on the optical flow experiments on several benchmark datasets show that ouralgorithm is effective with satisfying results

1 Introduction

Nowadays more and more surveillance cameras have beenused in public places Behavior analysis in crowded scenes[1ndash5] becomes more and more popular and important forpublic safety In order to eliminate the world representationlayer which can be a significant source of errors for algorithmmodeling an approach based on modeling directly at thepixel level was described in [6] In [7 8] social force modelwas used in abnormal crowd behavior detection In [910] a model named social attribute-aware force model wasproposed In this model in order to improve the algorithmperformance for the interaction behavior of the crowd socialcharacteristics of crowd behavior were taken into account

In [11] SIFT features were extracted for the Bag of Words(Bow) model with Spatial Pyramid Matching Kernel (SPM)Then a SVM classifier was used for cross-scene abnormalevents detection In [12] based on the fact that the occurrenceof abnormal events is rare while the frequently occurringevents are normal in general human perception proximityclustering for abnormal events detection in video sequencewas proposed In [13] when labeled information aboutnormal events was limited and information about abnormal

events was not available projection subspace associatedwith detectors was discovered by using both labeled andunlabeled segments In wireless sensor networks a fact hasbeen observed that instead of being transient most abnormalevents persist over a considerable period time Thus atechnique for handling data in a segment-based manner wasintroduced in [14] Without using any tracking and motionfeatures a feature extraction and events detection methodwere presented in [15] where features were extracted fromforeground blobs and then confined in SVM based modelsfor real-time events detection

Unlike most existing approaches used for abnormalevents detection sparse representation based approachesattracted many researchers in the recent years In [16] amethod to detect abnormal events by a sparse subspaceclustering was proposed In [17 18] a model based on theoptical flow was described which utilized the sparse recon-struction cost (SRC) over the normal dictionary to measurethe normalness of the tested samples As we know opticalflow is the approximatedmotion vector at each pixel locationwhich can reflect the relative distances of moving objectsTherefore it is important and useful in video surveillanceand abnormal events detection Other methods based on the

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015 Article ID 406941 11 pageshttpdxdoiorg1011552015406941

2 International Journal of Distributed Sensor Networks

HS

Optical flow fieldCompute HMOFP

in block sizeHMOFP

B

B

Frame i

Frame i + 1

Figure 1 The process for computing the HMOFP feature

histogram of optical flow were described in [19ndash21] Also itwas improved and used in this paper

Although the above approaches could successfully realizeabnormal events detection theywere limited in some aspectsSome models were established complicatedly and others costa long time in the detection process Based on these wepropose a novel detection model in crowded scenes which isrelatively simple and time-saving in calculation Similar to theapproach introduced in [21] our algorithm is mainly basedon a proper processing method in the optical flow field

The rest of the paper is organized as follows In Section 2we present how to acquire the motion features In Section 3the theory of one-class SVM is reviewed In Section 4 thealgorithm of abnormal events detection is introduced indetail Section 5 presents our experiment results Finallysome conclusions are presented in Section 6

2 Motion Feature Extraction

Optical flow field is the movement on the surface of grayscaleimages which reflects the movement information of twoconsecutive frames Optical flow provides the informationof direction and amplitude of the moving object in a scenewhich can describe the behavior of people very well Opticalflow is derived from the following basic equation

119868119909119906 + 119868119910V + 119868119905= 0 (1)

where 119868119909 119868119910 and 119868

119905are the partial derivatives of the image

grayscale value along the 119909 119910 and 119905 dimension respectively119906 and V are the horizontal (119909 dimension) and vertical (119910dimension) components of the optical flow Equation (1) isan ill-posed problem In [22] Horn and Schunck proposedan algorithm It is known as the HS algorithm to compute theoptical flow by introducing a global constraint of smoothnesswhich is equal to the additional condition

minnabla2119906 + nabla2V (2)

where nabla2119906 and nabla2V are Laplace operators of 119906 and VrespectivelyThe problem to get optical flow can be concludedas follows

min∬[(119868119909119906 + 119868119910V + 119868119905)2

+ 1205722

(nabla2

119906 + nabla2V)2

] 119889119909 119889119910 (3)

where 120572 is the parameter that represents the weights of theregularization term Then the Euler-Lagrange equations canbe acquired which are solved by utilizing the Gauss-Seidel

method It can get an iterative result to compute the opticalflow

119906119899+1

= 119906119899

minus

119868119909(119868119909119906119899

+ 119868119910V119899 + 119868

119905)

1205722 + 119868119909

2

+ 119868119910

2

V119899+1 = V119899 minus119868119910(119868119909119906119899

+ 119868119910V119899 + 119868

119905)

1205722 + 119868119909

2

+ 119868119910

2

(4)

where 119906 and V are weighted average value of 119906 and Vrespectively which are calculated in a neighborhood aroundthe pixel location 119899 denotes the algorithm iteration number

In this paper we propose a novel motion feature descrip-tor called histogram of maximal optical flow projection(HMOFP) Figure 1 briefly shows the process for computingthe HMOFP

As shown in Figure 2 the optical flow field of frame 119904 isdivided into 119898 image patches with overlap areas Each blockcontains 119861 times 119861 pixels Then we deal with the optical flow ineach patch as follows 0∘ndash360∘ are segmented into 119901 bins Foran image patch the optical flow vector of each pixel mustbelong to a bin according to its directionThus each bin maycontain several optical flowvectorsWeproject all optical flowvectors in the same bin onto the angle bisector of this binThen the maximal projection vector is selected as the featuredescriptor For example in Figure 3(a) there are two vectors997888997888rarr1199001198991and 997888997888rarr119900119899

2falling into the first bin It is easy to know that

the projection of 997888997888rarr1199001198992is longer than the projection of 997888997888rarr119900119899

1

Thus the length of the projection vector997888997888rarr11990011989921015840 is selected as the

feature descriptor of the first bin After computing119898 patcheswe obtain the feature descriptor vector of each image patchdenoted as [ℎ

1 ℎ2 ℎ

119898]119901times119898

where ℎ119894= [ℎ1

119894 ℎ

119901

119894]119901times1

For the 119894th patch ℎ119895

119894 1 le 119895 le 119901 1 le 119894 le 119898 denotes the

maximal amplitude among all projection vectors in the 119895thbin As shown in Figure 3(b) we take the concatenation ofthe 119898 feature descriptor vectors which is named 119867

119904 as the

global HMOFP feature of the frame 119904In order to describe a crowd scene well sufficient crowd

movement information is required On the other hand fordistinguishing two different scenes detailed comparisons ofthem are needed and useless information in these two scenesshould be eliminated In the classification process overlap-ping block-division can increase the number of significantmotion features in two different frames such that these twoframes can be more distinguishable Thus it is adopted inour algorithm since the optical information can be utilizedsufficiently Moreover to describe the motion of a crowd

International Journal of Distributed Sensor Networks 3

Block m

Block 1

BB

B middot middot middot

middot middot middot

Figure 2 Block-division of the optical flow field belonging to the frame 119904

p

1

2

6

5

4

3

O

Angle bisector

n1

n2

n1998400

n2998400


(a)

1 2 p 1 2 p 1 2 p 1 2 p

h1 h2 h3 hm

Hs

middot middot middot middot middot middot middot middot middot middot middot middotmiddot middot middot

(b)

Figure 3 (a) The calculation of HMOFP in each bin (b) Components of the global feature descriptor of the frame 119904

we need two factors explicit directions and the movingdistance along each direction The operation of segmentingthe 2119863 space into 119901 bins provides us ample information todescribe the directions of moving people To let the directionin each bin be unique we select the 119901 angle bisectors asthe direction standard Since there may be far more thanone optical flow vector in each bin in order to enhancethe distinction between the normal scene and the abnormalscene we select the maximal vector projection rather thanthe sum of all the vector projections on the bisector asthe motion feature descriptor If we ignore the backgroundarea the amplitudes of motion vectors that belong to thenormal area are very small in a normal frame and the motionvectors corresponding to the abnormal area are large inan abnormal frame Usually the number of normal motionvectors is much more than that of the abnormal area If weuse the sum of all projection vectors on the angel bisectoras the feature descriptor of each bin the accumulation ofthe massive small motion vectors in the normal frame mayconfuse the small number of large motion vectors in theabnormal frame that is the sum of all projection vectors onthe angel bisector in each bin of the normal frame is likely

to be close to that of the abnormal frame Thus in orderto improve the distinguishability between the abnormal andnormal frames we select the maximal projection vector asthe feature descriptor of each bin as it was demonstrated inFigure 3

3 One-Class SVM

SVM was initiated by Vapnik and Lerner [23] Since thekernel methods were introduced SVM has been appliedextensively in nonliner classification problems [24ndash26] Inone-class classification problem the substance is that theboundary that is an appropriate region needs to be deter-mined in the data space X which contains most of thesamples coming from an unknown probability distribution119863 This goal can be realized by searching for an optimaldecision hyperplane in the feature space which is knownas the Hilbert space H This hyperplane can maximize thedistance between itself and the original point while only asmall part of data falls between them [27] The relationshipbetweenX andH is shown in Figure 4


Boundary

(a)

Original point

Hyperplane

(b)

Figure 4The correspondence between data space and feature space (a)The boundary in the data spaceX (b)The hyperplane in the featurespaceH

One-class SVMproblem can be presented as an optimiza-tion model

min119908120585120588

1

2(wTw) minus 120588 + 1

]119897(

119897

sum

119894=1

120585119894)

st wT120601 (x119894) ge 120588 minus 120585

119894 120585119894ge 0

(5)

where x119894isin X 119894 isin [1 sdot sdot sdot 119897] are training samples in the input

data space X and 120601 X rarr H can map a vector x119894into the

feature spaceH wT120601(x119894) minus 120588 = 0 is the decision hyperplane

120585119894is the slack variable for penalizing the outliers ] isin (0 1]

is the hyperparameter which is the weight for controllingslack variable and tunes the number of acceptable outliers120601 is a mapping function which provides us a way to solve thenonlinear classification problem in the space X by a linearsolution in the space H By calculating dot product in Hthe kernel function is defined as 119896(x

119894 x119895) = 120601

T(x119894)120601(x119895) The

decision function in the spaceXwith a Lagrangianmultiplier120572119894is defined as

119891 (x) = sgn(119897

sum

119894=1

120572119894119896 (x119894 x) minus 120588) (6)

In [28] it was introduced that if appropriate parameters wereselected polynomial and sigmoid kernels will result in similarresults with Gaussian We choose Gaussian kernel in ouralgorithmThis kernel is defined as

119896 (x119894 x119895) = exp(minus

10038171003817100381710038171003817x119894minus x119895

10038171003817100381710038171003817

2

21205902) (7)

where x119894and x119895belong to the spaceX and 120590 is the scale factor

at which the data should be clusteredIn our method one-class SVM is utilized as follows

Firstly the training set is used to establish a model Then anappropriate boundary in the data space can be determinedThe new incoming frames will be clustered by the followingrule if theHMOFP feature of the testing frame falls inside theboundary it will be clustered as a normal frame Otherwiseit is abnormal

4 Abnormal Events Detection

In this section an algorithm for abnormal events detectionin surveillance video is described in detail Suppose that for agiven scene there is a set of training frames [119891

1 119891

119897] which

describe the normal behavior of crowded peopleThe generalprocedures for the abnormal events detection based on thehistogram of maximal optical flow projection (HMOFP) arepresented as follows

Step 1 Calculate the optical flow that is [OP1 OP

119897minus1] by

the HS method at each pixel of the first 119897 minus 1 frames

[1198911 119891

119897]119886times119887times119897

HS997888rarr [OP

1 OP

119897minus1]119886times119887times(119897minus1)

(8)

where 119886 times 119887 is the size of the frame image and 119897 is the numberof the frames in the training set Our method to computeoptical flow is based on the two consecutive frames whichis only effective to the first frame so in the right side of (8)the maximal subscript is 119897 minus 1

Step 2 Extract the motion features of the first 119897 minus 1 trainingframes Then the HMOFP feature vectors of them canbe obtained which is denoted as the set [119867

1 119867

119897minus1]T

Consider[OP1 OP


HMOFP997888997888997888997888997888997888rarr [119867

1 119867

119897minus1]T119901times119898times(119897minus1)

(9)

Step 3 Based on HMOFP one-class SVM is utilized tocalculate the optimal boundary of the set [119867

1 119867

119897minus1]T

which corresponds to the set of support vectors or the optimalhyperplane in the feature space

Step 4 Detect HMOFP of the testing frames based on themodel trained by the motion feature of the first 119897 minus 1 trainingframes

The whole procedure is illustrated in Figure 5

5 Experimental Results

In this section based on theUMNdataset [29] andPETS2009dataset [30] we evaluate our method for abnormal event


Optical flow field

resultDetection

train

HMOFP

HMOFP One-class SVM

HS

HS

Motion feature

Optical flow field

Testing set modelClassification

Motion featureTraining set

Figure 5 The flowchart of the proposed abnormal events detection algorithm

(a) Normal event

Abnormal crowd activity

(b) Abnormal event

Figure 6 Two different scenes in the sequence of lawn

Frame 1 Frame 1452

Ground truth

Detecting result

NormalAbnormal

Figure 7 Classification results of the lawn scene

detection Image patch size is set as 64 times 64 and 128 times 128respectively in the UMN dataset and PETS2009 dataset 0∘ndash360∘ are divided into 18 bins that is 119901 = 18 The overlappingproportion of two neighboring blocks is 50 In the UMNdataset the length of the HMOFP feature of each frame is972 with a 320times240 resolution In the PETS2009 dataset theresolution of each frame is 768 times 576 and the length of theHMOFP feature is 1584

51 Experiments on the UMNDataset There are three differ-ent crowded scenes in the UMN dataset which are namedlawn indoor and plaza respectively In our experiments weselect a part of the normal frames of each scene as the trainingset and take the rest of the video sequence as the testing set

511 Detection in the Lawn Scene The video sequence of thelawn scene contains 1453 frames in total The first 480 framesare taken as the training set As shown in Figure 6 in the lawn

scene the normal event is that individuals walk in differentdirections The abnormal event is that individuals suddenlyrun away The detection results of the lawn scene are shownin Figure 7 The accuracy of the detection results is 955141

512 Detection in the Indoor Scene The video sequence ofthe indoor scene contains 4144 frames in total The first 319frames are taken as the training set As shown in Figure 8 inthe indoor scene the normal event is that some people aretalking and standing in a relatively fixed location while someothers are walking along the road in the hall The abnormalevent is that people run out of the doors suddenly Thedetection results of the indoor scene are shown in Figure 9The accuracy of the detection results is 912857

513 Detection in the Plaza Scene The video sequence ofthe plaza scene contains 2412 frames in total The first 550frames are taken as the training set As shown in Figure 10 in


(a) Normal event


(b) Abnormal event

Figure 8 Two different scenes in the sequence of indoor

Frame 1 Frame 4143

Ground truth

Detecting result

NormalAbnormal

Figure 9 Classification results of the indoor scene

(a) Normal event


(b) Abnormal event

Figure 10 Two different scenes in the sequence of plaza

the plaza scene the normal event is that people walk aroundthe center of the square The abnormal event is that peoplesuddenly run away from the square The detection results ofthe plaza scene are shown in Figure 11 The accuracy of thedetection results is 943352

52 Experiments on the PETS2009 Dataset In the followingexperiments we can choose some specific scenes we areinterested in as the targets in the detection progress In thePETS2009 dataset we firstly select the training set and thenormal testing set respectively in the same scene Thenanother video clip in a different scene is taken as thecorresponding abnormal testing set Our experiments andthe detection results are shown as follows

521 People Scatter Detection In this part the training setis the video sequence Time 14-16 (Frame 0 to Frame 222)where people are walking or running towards one directionThe normal testing set includes 41 frames (Frame 48 to Frame88) ofTime 14-17 41 frames (Frame 337 to Frame 377) ofTime14-33 are labeled as abnormal for testing in which people arescattered in all directionsThe two different scenes are shownin Figure 12 The accuracy of the detection results is 975 asshown in Figure 13

522 Crowd Movement Direction Detection In this part thetraining set is the video sequence Time 14-55 (Frame 0 toFrame 399) where people are walking towards all directionsThe normal testing set includes 89 frames (Frame 400 to


Frame 2141Frame 1

Ground truth

Detecting result

NormalAbnormal

Figure 11 Classification results of the plaza scene

(a) Normal scene (b) Abnormal scene

Figure 12 Two different scenes in the same location

PETS2009 time 14-33

Detecting resultGround truth

minus15

minus1

minus05

0

05

1

15

Labe

l of d

etec

ted

fram

es

0 10 20 30 40 50 60 70 80 90minus10

Number of detected frames

Figure 13 The detection results of the sequence Time 14-33 ldquo1rdquo means normal and ldquominus1rdquo means abnormal

Frame 488) of Time 14-55 89 frames (Frame 0 to Frame 88)of the video sequence Time 14-17 are labeled as abnormal fortesting in which people are walking towards one directionThe two different scenes are shown in Figure 14The accuracyof the detection results is 926136 as shown in Figure 15

523 People Running Detection In this part the trainingset contains 50 frames (Frame 0 to Frame 49) of the video

sequence Time 14-31 and 61 frames (Frame 0 to Frame 60) ofthe video sequenceTime 14-17 where people arewalking fromright to left and from left to right respectively The normaltesting set includes 104 frames (Frame 0 to Frame 37 andFrame 108 to Frame 173) of Time 14-16 119 frames (Frame 38to Frame 107 and Frame 174 to Frame 222) of Time 14-16 arelabeled as abnormal for testing in which people are runningtowards one direction The two different scenes are shown in




PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el


Figure 16 The accuracy of the detection results is 936937as shown in Figure 17

524 People Splitting Detection In this part the training setcontains Frames 0 to 40 of the video sequence Time 14-16where people are walking towards the same direction Thenormal testing set includes 64 frames (Frame 0 to Frame 63)of Time 14-31 66 frames (Frame 64 to Frame 129) of the videosequence Time 14-31 are labeled as abnormal for testing inwhich the crowd is splittingThe normal scene and abnormalscene are shown in Figure 18 The accuracy of the detectionresults is 961538 as shown in Figure 19

525 Comparison We compared our algorithmwith the his-togram of optical flow orientation (HOFO)method proposedin [21] as shown in Table 1 Most results of our algorithm arebetter than those of HOFO

Table 1 The comparison of HMOFP with HOFO

AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31

MethodHOFO 975 90 9324 946154HMOFP (ours) 975 926136 936937 961538

6 Conclusion

In this paper we proposed an algorithm for abnormal eventsdetection in crowded scenes with global-frame scale Ourmethod contains two main procedures first is computingthe histogram of maximal optical flow projection (HMOFP)descriptor of the input video sequence Second one-classSVM classifier is utilized for nonlinear classification of the




PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el


testing sets The proposed method has been tested on severalsurveillance video datasets with good detection accuracy

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This work is supported by the NSFC (nos 61273274 6137012761572067 and 61272028) 973 Program (no 2011CB302203)National Key Technology RampD Program of China (nos2012BAH01F03 NSFB4123104 FRFCU 2014JBZ004 andZ131110001913143) and Tsinghua-Tencent Joint Lab for IIT

References

[1] R Mehran B E Moore and M Shah ldquoA streakline repre-sentation of flow in crowded scenesrdquo in Computer VisionmdashECCV 2010 11th European Conference on Computer VisionHeraklion Crete Greece September 5ndash11 2010 Proceedings PartIII vol 6313 of Lecture Notes in Computer Science pp 439ndash452Springer Berlin Germany 2010

[2] Y Cong J Yuan and Y Tang ldquoVideo anomaly search incrowded scenes via spatio-temporal motion contextrdquo IEEETransactions on Information Forensics and Security vol 8 no10 pp 1590ndash1599 2013

[3] F Daniyal and A Cavallaro ldquoAbnormal motion detection incrowded scenes using local spatio-temporal analysisrdquo in Pro-ceedings of the 36th IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo11) pp 1944ndash1947 May2011

[4] W Li V Mahadevan and N Vasconcelos ldquoAnomaly detectionand localization in crowded scenesrdquo IEEE Transactions onPattern Analysis and Machine Intelligence vol 36 no 1 pp 18ndash32 2014

[5] M Thida H-L Eng and P Remagnino ldquoLaplacian eigenmapwith temporal constraints for local abnormality detection incrowded scenesrdquo IEEE Transactions on Cybernetics vol 43 no6 pp 2147ndash2156 2013

[6] D Kosmopoulos and S P Chatzis ldquoRobust visual behaviorrecognitionrdquo IEEE Signal Processing Magazine vol 27 no 5 pp34ndash45 2010

[7] RMehran A Oyama andM Shah ldquoAbnormal crowd behaviordetection using social force modelrdquo in Proceedings of the IEEEConference on Computer Vision and Pattern Recognition (CVPRrsquo09) pp 935ndash942 IEEE Miami Fla USA June 2009

[8] S-H Yen and C-H Wang ldquoAbnormal event detection usingHOSFrdquo in Proceedings of the 3rd International Conference on ITConvergence and Security (ICITCS rsquo13) pp 1ndash4 IEEE MacaoChina December 2013

[9] Y Zhang L Qin H Yao and Q Huang ldquoAbnormal crowdbehavior detection based on social attribute-aware forcemodelrdquoin Proceedings of the 19th IEEE International Conference onImage Processing (ICIP rsquo12) pp 2689ndash2692 October 2012

[10] Y Zhang L Qin R Ji H Yao and Q Huang ldquoSocial attribute-aware force model exploiting richness of interaction for abnor-mal crowddetectionrdquo IEEETransactions onCircuits and Systemsfor Video Technology vol 25 no 7 pp 1231ndash1245 2015

[11] T-Y Hung J Lu and Y-P Tan ldquoCross-scene abnormal eventdetectionrdquo in Proceedings of the IEEE International Symposiumon Circuits and Systems (ISCAS rsquo13) pp 2844ndash2847 May 2013

[12] T SandhanA Sethi T Srivastava and J YChoi ldquoUnsupervisedlearning approach for abnormal event detection in surveillancevideo by revealing infrequent patternsrdquo in Proceedings of the28th International Conference on Image and Vision ComputingNew Zealand (IVCNZ rsquo13) pp 494ndash499 November 2013

[13] I Tziakos A Cavallaro and L Xu ldquoLocal abnormal detectionin video using subspace learningrdquo in Proceedings of the IEEEInternational Conference on Advanced Video and Signal BasedSurveillance (AVSS rsquo10) pp 519ndash525 2010

[14] M Xie J Hu and S Guo ldquoSegment-based anomaly detectionwith approximated sample covariance matrix in wireless sensor


networksrdquo IEEE Transactions on Parallel and Distributed Sys-tems vol 26 no 2 pp 574ndash583 2015

[15] MHaque andMMurshed ldquoPanic-driven event detection fromsurveillance video streamwithout track andmotion featuresrdquo inProceedings of the IEEE International Conference onMultimediaand Expo (ICME rsquo10) pp 173ndash178 IEEE Singapore July 2010

[16] H Ren and T B Moeslund ldquoAbnormal event detection usinglocal sparse representationrdquo in Proceedings of the 11th IEEEInternational Conference on Advanced Video and Signal-BasedSurveillance (AVSS rsquo14) pp 125ndash130 IEEE Seoul Republic ofKorea August 2014

[17] Y Cong J Yuan and J Liu ldquoSparse reconstruction cost forabnormal event detectionrdquo in Proceedings of the IEEE Confer-ence on Computer Vision and Pattern Recognition (CVPR rsquo11)pp 3449ndash3456 June 2011

[18] Y Cong J Yuan and J Liu ldquoAbnormal event detection incrowded scenes using sparse representationrdquo Pattern Recogni-tion vol 46 no 7 pp 1851ndash1864 2013

[19] TWang andH Snoussi ldquoHistograms of optical flow orientationfor visual abnormal events detectionrdquo in Proceedings of the IEEE9th International Conference on Advanced Video and Signal-Based Surveillance (AVSS rsquo12) pp 13ndash18 September 2012

[20] TWang andH Snoussi ldquoHistograms of optical flow orientationfor abnormal events detectionrdquo in Proceedings of the IEEEInternational Workshop on Performance Evaluation of Trackingand Surveillance (PETS rsquo13) pp 45ndash52 January 2013

[21] T Wang and H Snoussi ldquoDetection of abnormal visual eventsvia global optical flow orientation histogramrdquo IEEE Transac-tions on Information Forensics and Security vol 9 no 6 pp 988ndash998 2014

[22] B K P Horn and B G Schunck ldquoDetermining optical flowrdquoArtificial Intelligence vol 17 no 1ndash3 pp 185ndash203 1981

[23] V N Vapnik and A Lerner ldquoPattern recognition using general-ized portrait methodrdquo Automation and Remote Control vol 24no 6 pp 774ndash780 1963

[24] B E Boser I M Guyon and V N Vapnik ldquoTraining algorithmfor optimal margin classifiersrdquo in Proceedings of the 5th AnnualACM Workshop on Computational Learning Theory pp 144ndash152 July 1992

[25] C Piciarelli C Micheloni and G L Foresti ldquoTrajectory-basedanomalous event detectionrdquo IEEE Transactions on Circuits andSystems for Video Technology vol 18 no 11 pp 1544ndash1554 2008

[26] N Cristianini and J Shawe-Taylor An Introduction to SupportVector Machines and Other Kernel-based Learning MethodsCambridge University Press Cambridge UK 2000

[27] B Scholkopf J C Platt J Shawe-Taylor A J Smola and RC Williamson ldquoEstimating the support of a high-dimensionaldistributionrdquo Neural Computation vol 13 no 7 pp 1443ndash14712001

[28] B Scholkopf and A J Smola Learning with Kernels SupportVector Machines Regularization Optimization and BeyondMIT Press Cambridge Mass USA 2002

[29] UMN ldquoUnusual crowd activity dataset of university of min-nesota department of computer science and engineeringrdquo2006

[30] PETS ldquoPerformance evaluation of tracking and surveillance(pets) 2009 benchmark data multisensor sequences containingdifferent crowd activitiesrdquo 2009 httpwwwcvgreadingacukPETS2009ahtml

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Navigation and Observation



DistributedSensor Networks



HS

Optical flow fieldCompute HMOFP

in block sizeHMOFP

B

B

Frame i

Frame i + 1

Figure 1 The process for computing the HMOFP feature

histogram of optical flow were described in [19ndash21] Also itwas improved and used in this paper

Although the above approaches could successfully realizeabnormal events detection theywere limited in some aspectsSome models were established complicatedly and others costa long time in the detection process Based on these wepropose a novel detection model in crowded scenes which isrelatively simple and time-saving in calculation Similar to theapproach introduced in [21] our algorithm is mainly basedon a proper processing method in the optical flow field

The rest of the paper is organized as follows In Section 2we present how to acquire the motion features In Section 3the theory of one-class SVM is reviewed In Section 4 thealgorithm of abnormal events detection is introduced indetail Section 5 presents our experiment results Finallysome conclusions are presented in Section 6

2 Motion Feature Extraction

Optical flow field is the movement on the surface of grayscaleimages which reflects the movement information of twoconsecutive frames Optical flow provides the informationof direction and amplitude of the moving object in a scenewhich can describe the behavior of people very well Opticalflow is derived from the following basic equation

119868119909119906 + 119868119910V + 119868119905= 0 (1)

where 119868119909 119868119910 and 119868

119905are the partial derivatives of the image

grayscale value along the 119909 119910 and 119905 dimension respectively119906 and V are the horizontal (119909 dimension) and vertical (119910dimension) components of the optical flow Equation (1) isan ill-posed problem In [22] Horn and Schunck proposedan algorithm It is known as the HS algorithm to compute theoptical flow by introducing a global constraint of smoothnesswhich is equal to the additional condition

minnabla2119906 + nabla2V (2)

where nabla2119906 and nabla2V are Laplace operators of 119906 and VrespectivelyThe problem to get optical flow can be concludedas follows

min∬[(119868119909119906 + 119868119910V + 119868119905)2

+ 1205722

(nabla2

119906 + nabla2V)2

] 119889119909 119889119910 (3)

where 120572 is the parameter that represents the weights of theregularization term Then the Euler-Lagrange equations canbe acquired which are solved by utilizing the Gauss-Seidel

method It can get an iterative result to compute the opticalflow

119906119899+1

= 119906119899

minus

119868119909(119868119909119906119899

+ 119868119910V119899 + 119868

119905)

1205722 + 119868119909

2

+ 119868119910

2

V119899+1 = V119899 minus119868119910(119868119909119906119899

+ 119868119910V119899 + 119868

119905)

1205722 + 119868119909

2

+ 119868119910

2

(4)

where 119906 and V are weighted average value of 119906 and Vrespectively which are calculated in a neighborhood aroundthe pixel location 119899 denotes the algorithm iteration number

In this paper we propose a novel motion feature descrip-tor called histogram of maximal optical flow projection(HMOFP) Figure 1 briefly shows the process for computingthe HMOFP

As shown in Figure 2 the optical flow field of frame 119904 isdivided into 119898 image patches with overlap areas Each blockcontains 119861 times 119861 pixels Then we deal with the optical flow ineach patch as follows 0∘ndash360∘ are segmented into 119901 bins Foran image patch the optical flow vector of each pixel mustbelong to a bin according to its directionThus each bin maycontain several optical flowvectorsWeproject all optical flowvectors in the same bin onto the angle bisector of this binThen the maximal projection vector is selected as the featuredescriptor For example in Figure 3(a) there are two vectors997888997888rarr1199001198991and 997888997888rarr119900119899

2falling into the first bin It is easy to know that

the projection of 997888997888rarr1199001198992is longer than the projection of 997888997888rarr119900119899

1

Thus the length of the projection vector997888997888rarr11990011989921015840 is selected as the

feature descriptor of the first bin After computing119898 patcheswe obtain the feature descriptor vector of each image patchdenoted as [ℎ

1 ℎ2 ℎ

119898]119901times119898

where ℎ119894= [ℎ1

119894 ℎ

119901

119894]119901times1

For the 119894th patch ℎ119895

119894 1 le 119895 le 119901 1 le 119894 le 119898 denotes the

maximal amplitude among all projection vectors in the 119895thbin As shown in Figure 3(b) we take the concatenation ofthe 119898 feature descriptor vectors which is named 119867

119904 as the

global HMOFP feature of the frame 119904In order to describe a crowd scene well sufficient crowd

movement information is required On the other hand fordistinguishing two different scenes detailed comparisons ofthem are needed and useless information in these two scenesshould be eliminated In the classification process overlap-ping block-division can increase the number of significantmotion features in two different frames such that these twoframes can be more distinguishable Thus it is adopted inour algorithm since the optical information can be utilizedsufficiently Moreover to describe the motion of a crowd


Block m

Block 1

BB




p

1

2

6

5

4

3

O

Angle bisector

n1

n2

n1998400

n2998400


(a)

1 2 p 1 2 p 1 2 p 1 2 p

h1 h2 h3 hm

Hs


(b)




3 One-Class SVM



Boundary

(a)

Original point

Hyperplane

(b)



min119908120585120588

1

2(wTw) minus 120588 + 1

]119897(

119897

sum

119894=1

120585119894)

st wT120601 (x119894) ge 120588 minus 120585

119894 120585119894ge 0

(5)






119894 x119895) = 120601

T(x119894)120601(x119895) The


119891 (x) = sgn(119897

sum

119894=1

120572119894119896 (x119894 x) minus 120588) (6)


119896 (x119894 x119895) = exp(minus

10038171003817100381710038171003817x119894minus x119895

10038171003817100381710038171003817

2

21205902) (7)






1 119891

119897] which



119897minus1] by


[1198911 119891

119897]119886times119887times119897

HS997888rarr [OP

1 OP


(8)



1 119867

119897minus1]T

Consider[OP1 OP


HMOFP997888997888997888997888997888997888rarr [119867

1 119867


(9)


1 119867

119897minus1]T







Optical flow field

resultDetection

train

HMOFP

HMOFP One-class SVM

HS

HS

Motion feature

Optical flow field




(a) Normal event


(b) Abnormal event


Frame 1 Frame 1452

Ground truth

Detecting result

NormalAbnormal









(a) Normal event


(b) Abnormal event


Frame 1 Frame 4143

Ground truth

Detecting result

NormalAbnormal


(a) Normal event


(b) Abnormal event







Frame 2141Frame 1

Ground truth

Detecting result

NormalAbnormal




PETS2009 time 14-33


minus15

minus1

minus05

0

05

1

15

Labe

l of d

etec

ted

fram

es

0 10 20 30 40 50 60 70 80 90minus10









PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el






AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31


6 Conclusion





PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Block m

Block 1

BB




p

1

2

6

5

4

3

O

Angle bisector

n1

n2

n1998400

n2998400


(a)

1 2 p 1 2 p 1 2 p 1 2 p

h1 h2 h3 hm

Hs


(b)




3 One-Class SVM



Boundary

(a)

Original point

Hyperplane

(b)



min119908120585120588

1

2(wTw) minus 120588 + 1

]119897(

119897

sum

119894=1

120585119894)

st wT120601 (x119894) ge 120588 minus 120585

119894 120585119894ge 0

(5)






119894 x119895) = 120601

T(x119894)120601(x119895) The


119891 (x) = sgn(119897

sum

119894=1

120572119894119896 (x119894 x) minus 120588) (6)


119896 (x119894 x119895) = exp(minus

10038171003817100381710038171003817x119894minus x119895

10038171003817100381710038171003817

2

21205902) (7)






1 119891

119897] which



119897minus1] by


[1198911 119891

119897]119886times119887times119897

HS997888rarr [OP

1 OP


(8)



1 119867

119897minus1]T

Consider[OP1 OP


HMOFP997888997888997888997888997888997888rarr [119867

1 119867


(9)


1 119867

119897minus1]T







Optical flow field

resultDetection

train

HMOFP

HMOFP One-class SVM

HS

HS

Motion feature

Optical flow field




(a) Normal event


(b) Abnormal event


Frame 1 Frame 1452

Ground truth

Detecting result

NormalAbnormal









(a) Normal event


(b) Abnormal event


Frame 1 Frame 4143

Ground truth

Detecting result

NormalAbnormal


(a) Normal event


(b) Abnormal event







Frame 2141Frame 1

Ground truth

Detecting result

NormalAbnormal




PETS2009 time 14-33


minus15

minus1

minus05

0

05

1

15

Labe

l of d

etec

ted

fram

es

0 10 20 30 40 50 60 70 80 90minus10









PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el






AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31


6 Conclusion





PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Boundary

(a)

Original point

Hyperplane

(b)



min119908120585120588

1

2(wTw) minus 120588 + 1

]119897(

119897

sum

119894=1

120585119894)

st wT120601 (x119894) ge 120588 minus 120585

119894 120585119894ge 0

(5)






119894 x119895) = 120601

T(x119894)120601(x119895) The


119891 (x) = sgn(119897

sum

119894=1

120572119894119896 (x119894 x) minus 120588) (6)


119896 (x119894 x119895) = exp(minus

10038171003817100381710038171003817x119894minus x119895

10038171003817100381710038171003817

2

21205902) (7)






1 119891

119897] which



119897minus1] by


[1198911 119891

119897]119886times119887times119897

HS997888rarr [OP

1 OP


(8)



1 119867

119897minus1]T

Consider[OP1 OP


HMOFP997888997888997888997888997888997888rarr [119867

1 119867


(9)


1 119867

119897minus1]T







Optical flow field

resultDetection

train

HMOFP

HMOFP One-class SVM

HS

HS

Motion feature

Optical flow field




(a) Normal event


(b) Abnormal event


Frame 1 Frame 1452

Ground truth

Detecting result

NormalAbnormal









(a) Normal event


(b) Abnormal event


Frame 1 Frame 4143

Ground truth

Detecting result

NormalAbnormal


(a) Normal event


(b) Abnormal event







Frame 2141Frame 1

Ground truth

Detecting result

NormalAbnormal




PETS2009 time 14-33


minus15

minus1

minus05

0

05

1

15

Labe

l of d

etec

ted

fram

es

0 10 20 30 40 50 60 70 80 90minus10









PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el






AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31


6 Conclusion





PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Optical flow field

resultDetection

train

HMOFP

HMOFP One-class SVM

HS

HS

Motion feature

Optical flow field




(a) Normal event


(b) Abnormal event


Frame 1 Frame 1452

Ground truth

Detecting result

NormalAbnormal









(a) Normal event


(b) Abnormal event


Frame 1 Frame 4143

Ground truth

Detecting result

NormalAbnormal


(a) Normal event


(b) Abnormal event







Frame 2141Frame 1

Ground truth

Detecting result

NormalAbnormal




PETS2009 time 14-33


minus15

minus1

minus05

0

05

1

15

Labe

l of d

etec

ted

fram

es

0 10 20 30 40 50 60 70 80 90minus10









PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el






AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31


6 Conclusion





PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) Normal event


(b) Abnormal event


Frame 1 Frame 4143

Ground truth

Detecting result

NormalAbnormal


(a) Normal event


(b) Abnormal event







Frame 2141Frame 1

Ground truth

Detecting result

NormalAbnormal




PETS2009 time 14-33


minus15

minus1

minus05

0

05

1

15

Labe

l of d

etec

ted

fram

es

0 10 20 30 40 50 60 70 80 90minus10









PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el






AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31


6 Conclusion





PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Frame 2141Frame 1

Ground truth

Detecting result

NormalAbnormal




PETS2009 time 14-33


minus15

minus1

minus05

0

05

1

15

Labe

l of d

etec

ted

fram

es

0 10 20 30 40 50 60 70 80 90minus10









PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el






AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31


6 Conclusion





PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












PETS2009 time 14-17

20 40 60 80 100 120 140 160 1800Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el






AccuracySequence

Time14-33

Time14-17

Time14-16

Time14-31


6 Conclusion





PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












PETS2009 time 14-16


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el

50 100 150 200 2500Frame number





PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










PETS2009 time 14-31

20 40 60 80 100 120 1400Frame number


minus15

minus1

minus05

0

05

1

15

Fram

e lab

el





Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Documents

Research Article Histogram of Maximal Optical Flow Projection for Abnormal …downloads.hindawi.com/journals/ijdsn/2015/406941.pdf · 2015-11-24 · Research Article Histogram of