CAP 6412 Advanced Computer Vision - UCF Computer Sciencebgong/CAP6412/lec28.pdf · •Used Google...

CAP6412AdvancedComputerVision

http://www.cs.ucf.edu/~bgong/CAP6412.html

Boqing GongApril 21st,2016

Today

• Administrivia• Freeparametersinanapproach,model,oralgorithm?• Egocentricvideos byAisha

ProjectIIdue:nextWednesday(04/27,5PM)

• FinalProjectPresentation:04/28,1—3:50PM

• Latesubmissions:https://docs.google.com/spreadsheets/d/1uNPfUsdnw5xfzIV-PrQTo9xTWKfv7s-OPyuV_zZw9Fc/edit?usp=sharing

Today

• Administrivia• Freeparametersinanapproach,model,oralgorithm?• Egocentricvideos byAisha

Freeparameters(hyper-parameters)

• InProject2,whenyoutraintheCNNs• Learningrate,momentum,weightdecay,dropoutrate,earlystopping,etc.• Networkarchitecture,nonlinearfunctions,strides,etc.

• InLinearregression

• InSVM

minw

MX

m=1

(ym � x

Tmw) + �kwk22

minw,⇠m,m=1,··· ,M

MX

m=1

⇠m + �kwk22

s.t. ym(xTmw) � 1� ⇠m,& ⇠m � 0 8m

Freeparameters(hyper-parameters)

• InK-meansclustering:K,thenumberofclusters• InK-Nearestneighborsclassifier: K,thenumberofneighbors• InCannyedgedetection• Gaussianfilter,thresholds

• InR-CNN• Thresholdofselectivesearch• #Layers,filtersize,stride,wheremaxpooling• Paddingornot,learningrate,momentum,weightdecay,#iterations• Trade-offparameter• Featureselectionforregression• Batchsize

Freeparameters(hyper-parameters)

• Freeparameters vs.Modelparameters

• Oftenseekmodelparametersbyoptimization• Gradientdescent(GD),coordinatedescent,Newton,stochasticGD,etc.

• Howtochoosethefreeparameters?

minw

MX

m=1

(ym � x

Tmw) + �kwk22

Howtochoosethefreeparameters

• Smallesterrorrateon• Testset?• Validationset?

• Smallestexpectederrorrate ontheentirepopulation• Inpractice,however,wehaveaccesstoafinitesetofexamples!• Approximatetheexpectederrorrate• Choosefreeparameterswhichminimizetheapproximateerror

• Howtoapproximatetheexpectederror?

Weakapproximationoftheexpectederror!

Rarelyusedinpractice.

Popularforsmalldata.

Popularforsmalldata.

Popularforbigdata.

1. Dividedatatotraining,validation,andtest sets.2. Selectfreeparameters

1. E.g.,networklayers,#hiddenstates,nonlinearfunctions,etc.

3. Trainthemodelusingthetraining set4. Evaluatethemodelusingthevalidation set5. Repeatsteps2—4usingdifferentfreeparametersà

differentmodels6. Selectthebestmodel(andtheirassociatedfree

parameters)7. Trainthemodel(withtheassociatedfree

parameters)usingbothtraining andvalidation sets.8. Assessthisfinalmodelusingthetest set.

Skipstep7forbigdata.

Skipthisstepforbigdata.

Today

• Administrivia• Freeparametersinanapproach,model,oralgorithm?• Egocentricvideos byAisha

Hand detection in Egocentric videos

Aisha Urooj

Course Instructor: Dr. Boqing Gong Advanced Computer Vision

Motivation

• Emergence of new wearable technologies

– Action cameras

– Smart glasses, so on…

• These devices capture videos from first person’s perspective.

• Record user’s experiences

Image Source: [1]

An overview of First Person Vision

Image Credits: [1]

A hierarchical structure, starting from the raw video sequence (bottom) to the desired objectives (top)

Image Credits: [1]

Image Credits: [1]

Image

Credits:

[1]

Image Credits: [1]

Related Datasets [1]

Motivation

• Hands are very common in egocentric videos

• Appearance of hands and pose give important cues about human’s – actions

– attention

– Activity recognition

– user–machine interaction, so on.

• Most of the egocentric computer vision problems, from object detection to activity recognition requires accurate hand detection.

Challenges in hand detection

• Hands are highly deformable objects.

• Occlusion

• Cluttered background

• Dynamic background

• Inconsistent lighting

• Poor imaging conditions

• Highly dynamic camera motion

• So on..

Lending a Hand: Detecting Hands and

Recognizing Activities in Complex Egocentric

Interactions

Sven Bambach, Stefan Lee, David J. Crandall, Chen Yu

Indiana University

Outline

• Paper’s contribution

• Dataset details

• Approach

• Results

• Possible future directions

Paper’s Contributions

• Deep model for hand detection and classification in egocentric video, including fast domain-specific region proposals.

• A new technique for pixel wise hand segmentation.

• A quantitative analysis of how hand location and pose can be useful in accurate activity recognition.

• A large dataset of egocentric interactions with fine grained ground truth.

Overview

Image source: http://vision.soic.indiana.edu/projects/lending-a-hand/

Ground truth hand segmentation masks on sample frames from dataset.

A random

subset of

cropped

hands

according to

ground

Truth.

Dataset details

• 4 participants, 4 activities, 3 different locations (office, home, courtyard)

• Total 48 unique videos.

• Used Google Glass, 720x1280 at 30 fps.

• 2 persons in one video, each wearing google glass. (Synchronized video pairs and cut them to 90 seconds)

• Pixel level ground truth for over 15000 hand instances.

• Manual annotation of 100 frames/ video i.e. 4800 frames ground truth.

• Main Split: 36 training, 4 validation, 8 test videos.

Hand Detection: Approach

• Candidate windows generation

• Window classification using CNNs

Window Proposals Generation

• Probability that an object O appears in a region R of an image I.

• The proposed approach for candidate windows generation combines spatial biases and appearance models together.

Window Proposals Generation (Contd..)

• P (O) : Object occurrence probability

• P(R|O) : Probability that a certain region R (a bounding box) contains a specific hand (O)

• P(I | R, O): A pixel-level skin classifier – Estimates the probability that central pixel of R is

skin.

Coverage Results for Different Proposal Methods

Window classification

• A standard CNN classification framework used.

• CaffeNet from Caffe software package – Slight variation of AlexNet

• Each training batch contains equal number of samples from each class.

• Disabled horizontal and vertical flipping of sample images in Caffe – For differentiating between left and right hands.

Window classification (Contd..)

• The CNN weights are initialized from CaffeNet

– Except final fully connected layer which is set to zero mean gaussian.

• Fine-tuning using SGD

– Learning rate = 0.001

– Momentum = 0.999

Generate Spatially sampled window proposals

Classify window crops Using fine-tuned CNN

Perform non-maximum suppression for each test frame

Input

Hand Detection

• Two cases: – Detect hands of any type

– Detect hand of specific type (own left, your right etc.)

• PASCAL VOC criteria for scoring detections is used – Intersection over Union between the ground truth

and detected bounding box should be > 0.5

Precision-Recall curves for Hand detection

Qualitative Results for Hand Detection

Quantitative Results for Hand Detection

Hands Segmentation

• Pixelwise hand segmentation is useful for: – Hand pose recognition – In-hand object detection, so on..

• Goal: Label each pixel either to the background or to a specific hand class.

• Applied a semi-supervised segmentation algorithm

GrabCut. • Given an approximate foreground mask, GrabCut

iteratively refines foreground and background pixels , relabeling them using Markov Random Field.

Hands Segmentation

• For each hand detected bounding box, initial foreground estimation is computed using same color skin model.

• Thresholded and marked each pixel within the box as foreground except with very low skin probability.

• Run GrabCut algorithm on bounding box including padded region.

• Final segmentation is the union of the output masks for all detected bounding boxes.

Quantitative Results for Hand Segmentation

Two modes of possible failures

• Failure to properly detect hand bounding boxes.

• Inaccuracy in distinguishing hand pixels from background.

• Applying segmentation algorithm on ground truth bounding boxes results in raise to average 0.73

• Taking output of hand detector but using ground truth segmentation masks again increases average to 0.76

Qualitative Results for Hand Segmentation

Hand-based Activity Recognition

• Masked out all other non-hand background information by using ground truth hand segmentations.

• Fine-tuned a CNN to classify whole frames as one of the four activities.

– Training: 900 frames per activity for 36 videos

– Validation: 100 frames per activity for four videos

– Classification accuracy: 66.4% per frame

Hand-based Activity Recognition (contd..)

Incorporating temporal constraints:

• Simple voting based approach

• Classify each individual frame in the context of a fixed-size temporal window centered on the frame

• Scores are summed across the window

• Frame is labeled as the highest scoring class

Hand-based Activity Recognition

Some sample hand poses not present in their dataset

Related work on Egocentric Hands Detection

Work by A. Betancourt, University of Genoa, Italy 1)Hand Segmentation and tracking in FPV 2) A Sequential Classifier for Hand Detection in the Framework of

Egocentric Vision. CVPR 2014 3) The Evolution of First Person Vision Methods: A Survey. Observations:

– Misses detection of hands in many frames for other people. – Results show false positives in many frames. – No detection on hands shown in videos running within a video. – Segmentation is not efficient. – At times both hands are detected as either left or right. – Full arm is being considered as hand.

Possible Future Directions

• Improve segmentation technique

• Have an unbiased dataset

• Use an efficient tracking approach to incorporate temporal information

• Improve hand classifier

References

• [1] The Evolution of First Person Vision Methods:A Survey. A. Betancourt, P. Morerio, C. S. Regazzoni, and M. Rauterberg. IEEE Transactions on Circuits and Systems for Video Technology. Vol 25. Issue 5.

THANK YOU!