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A Generic Virtual Content Insertion SystemBased on Visual Attention Analysis
H. Liu1, 2, S. Jiang1, Q. Huang1, 2, C. Xu2, 3
1Institute of Computing Technology, Chinese Academy of Sciences2China-Singapore Institute of Digital Media
3National Lab of Pattern Recognition, Institute of Automation
Outline
Motivation Related work The proposed Virtual Content
Insertion (VCI) system Experimental results Conclusions
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Virtual Content Insertion
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Convenient Changeable Cost less
To construct a generic VCI system
Challenge
Advertisement insertion VS. Augmentation
Software based VS. Hardware based Challenge
Insertion time Insertion place Insertion method Insertion content
Related work– Insertion Time
Insert advertisements into video prologue Be neglected Insert the ad into
interesting segments Our method
Temporal attention Higher Attentive
Shots• K. Wan, C. Xu, “Automatic Content Placement in Sports Highlights”,
ICME, 2006.23/4/19 http://www.jdl.ac.cn 5
Related work– Insertion Place
Static region Color consistent
region Visual relevance
measure Lower informative
region Our method
Spatial attention Lower Attentive Region
• C. Xu, etc., “Implanting Virtual Advertisement into Broadcast Soccer Video”, PCM, 2004.
• K. Wan, etc., “Automatic Content Placement in Sports Highlights”, ICME, 2006.
• Y. Li, etc., “Real Time Advertisement Insertion in Baseball Video Based on Advertisement Effect”, ACM Multimedia, 2005.
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Related work – Insertion Method
Challenge Camera parameters
unknown Existing methods
Structure of the scene Predefined landmarks
Our method Affine transformation Global Motion Estimation
• X. Yu, etc., “Inserting 3D Projected Virtual Content into Broadcast Tennis Video”, ACM Multimedia 2006.
• C. Xu, etc., “Implanting Virtual Advertisement into Broadcast Soccer Video”, PCM, 2004.
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Related work – Insertion Content
Improve the ad effect Decrease intrusion VideoSense
Textual relevance Local visual and aural relevance
T. Mei, X-S. Hua, L. Yang, S. Li, “VideoSense-Towards Effective Online Video Advertising”, 16th ACM International Conference on Multimedia, pp: 1075-1084, 2007
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Outline
Motivation Related work The proposed VCI system Experimental results Conclusions
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T em poral A ttention A nalysis
Temporal Attention
Basic idea The more different a frame/shot/video
clip is to the preceding ones, the more probable for it to be attended
Measure Novelty
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, 1 min ,i t i tbin
diff F F H H
1
, ,t
t t i t ii t l
Nol F diff F F w F F
HAS Detection
Shot novelty
The longer a shot is, the more it is probable to be attended
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1
, ,t
t i t i ti t l
Nol S diff S S w S S
t t tAV S L Nol S
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Spatial Attention Analysis
Static attention Spatio-temporal attention
Motion saliency Static novelty
Static saliency calculation
Motion saliency
calculation
Static novelty calculation
Static saliency maps
Motion saliency maps
Static novelty maps
+Video Frames
Attention Maps
Static Saliency (1)
Psychological basis Contrast Information theory
Our method Contrast and information theory
Calculation Property of receptive field
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20, 0,DoG N N
Static Saliency (2)
Perceptive unit Pixel/block Region Object
Color quantization
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Static Saliency (3)
Contrast
Information density
Saliency
1
, ,K
ki
Con k d f k f i G i k
logID k p f k
Sal k Con k ID k
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Motion Saliency
Motion Vector Space HSV color space Angle H
Magnitude S
Texture V
Static Novelty (1)
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Static Novelty (2)
Static novelty: An event’s importance along temporal axis
Distance: KL 1X
log tt
t
M xNol t M x dx
M x
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Static LAR Detection
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Dynamic LAR Detection
1
L
M t tt
MAM P w t AM P
, 1
1
1k k
t
t Mk
P H P
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Affine transformation
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1
1 1
x
V y
f
2
2 2
x
V y
f
1 1 1r V V
2 2 2r V V
3 1 2r r r
1 2 3A r r r
1 10 0AP P A P A P
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Global Motion Estimation
1
, 1 0 00
t
t k k Ak
P H AP P AP
Outline
Motivation Related work The proposed VCI system Experimental results Conclusions
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Experiment Data Set – Test Video
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No. Video Genre Shot Time
1 Friendssituation comedy
200 11:25
2Children at
Housesituation comedy
200 14:48
3A Date with
LuYuInterview 200 20:49
4Adventure to the
westOutdoor teleplay
200 25:48
Sum ---- ---- 800 72:50
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Experiment Data Set -- Virtual Content
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Temporal attention & HAS (1)
0 10 20 30 40 500
0.5
1
Shot
Attention curve Notice Rate
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Temporal attention & HAS (2)
Noticing rate:
Consistency: the similarity between attention curve and noticing curve
in N
min ,k
cos AC k NC k
No. Consistency
1 0.75
2 0.79
3 0.84
4 0.82
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Temporal attention & HAS (3)
Relationship between noticing rate and attention value
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Spatial attention & LAR
Invited the users to evaluate the brands he/she has noticed in the video.
rate of GOOD 1 1
n n
i ii i
Rg Ng N
Video GOOD NEUTRAL BAD
1 72.25 19.13 8.62
2 70.87 23.13 6.00
3 66.25 25.00 8.75
4 70.38 25.62 4.00
Mean 69.94 23.22 6.84
Variance 6.67 8.55 5.20
Static Insertion Demo
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Dynamic Insertion Evaluation
Subjective evaluation Criteria
1. Is the result’s deformation consistent with the scene?
2. Does the inserted VC follow the camera motion?
3. To what degree the user is satisfied with the result?
Scores: 15
1 2 3 4 5 60
2
4
VCI Result
Eva
luat
ion
Mean Variance
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Dynamic Insertion Result
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Conclusion
Main contribution A generic virtual content insertion system. A new method of temporal attention and HAS
detection A new method of spatial attention and LAR
detection A dynamic insertion method
Future work The attention change caused by content insertion The interaction between insertion time and place