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SmartPlayer: User-Centric Video Fast-Forwarding K.-Y. Cheng, S.-J. Luo, B.-Y. Chen, and H.-H. Chu ACM CHI 2009 (international conference on Human factors.

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Presentation on theme: "SmartPlayer: User-Centric Video Fast-Forwarding K.-Y. Cheng, S.-J. Luo, B.-Y. Chen, and H.-H. Chu ACM CHI 2009 (international conference on Human factors."— Presentation transcript:

1 SmartPlayer: User-Centric Video Fast-Forwarding K.-Y. Cheng, S.-J. Luo, B.-Y. Chen, and H.-H. Chu ACM CHI 2009 (international conference on Human factors in computing systems)

2 Outline Introduction SmartPlayer – User-Centric Video Fast-Forwarding – Skimming Model – User Interface Results Conclusion

3 Introduction Microsoft Windows Media Player – Play, pause, stop, fast-forward, rewind/reverse video

4 Introduction Video summarization – Still-image abstraction —key frame extraction Ex: image mosaic – Video skimming Short video summary Video analysis techniques – Image/video features – Different video types

5 Introduction SmartPlayer – Adjust playback speed Complexity of the current scene Predefined semantic events – Learn user’s preferences About predefined semantic events User’s favorite playback speed – Play video continuously Not to miss any undefined events

6 Introduction SmartPlayer

7 User Behavior Observation And Inquiry User inquiry – 10 participants: 5 males and 5 females – How users fast-forwarding these videos? Video typeNumber of people who Fast-forward Surveillance video10 Sport video9 Movies0 Lecture videos2

8 User Behavior Observation And Inquiry User inquiry – surveillance, baseball, tennis, golf, and wedding videos – training videos – prototype player accelerate and decelerate (1~16x) Can jump to the normal speed One user’s watching pattern for a baseball video.

9 User-Centric Video Fast-Forwarding User behavior – Users tend to maintain a constant playback speed within a video shot. – Users prefer gradual increases of playback speed. – Users set the playback rate based on several minutes of recently viewed shots. SmartPlayer – Cut the video into segments – Adjust the playback speed gradually across segment boundaries – Speed control

10 Skimming Model Speed control – motion complexity – speed of the previous content

11 Skimming Model Motion layer – Color[1] detect shot boundaries – Motion extract optical flows between frames using the Lucas-Kanade method [1] Lienhart, R. Comparison of automatic shot boundary detection algorithms. SPIE Storage and Retrieval for Image and Video Databases VII 3656, (1999), 290-301.

12 Skimming Model Semantic layer – Extract semantic event points in video – Manual annotation Video typeEvents BaseballPitch, hit, homerun…… SurveillanceAppearance of pedestrians, cars, bicycles WeddingFormal wedding procedure NewsPolitical, financial, life, international event DramaNo event

13 Skimming Model

14 User Interface

15 Results Personalized adaptive fast-forwarding – 20 participants: 13 males and 7 females

16 Results Comparisons of different video players Video content understanding rateVideo watching time

17 Results Average rating of three types of video players

18 Results

19 Conclusion Automatically adapts its playback speed according to : – scene complexity – predefined events of interest – user’s preferences with respect to playback speed Learn user’s preferred event types and playback speeds for these event types Not skipping any segments

20 An Extended Framework for Adaptive Playback-Based Video Summarization Kadir A. Peker and Ajay Divakaran SPIE ITCOM 2003

21 Features Visual complexity – Motion activity: motion vector – Spatial complexity: DCT coefficient visual complexity=(motion vector) ‧ (DCT coefficient) For each DCT coefficient visual complexity= mean(cumulative energy at each visual complexity value) For each frame

22 Features Audio classes – 1-s segments – GMM-based classifiers – Silence, ball hit, applause, female speech, male speech, speech and music, music, and noise – Sport highlights detection Face detection – Viola-Jones face detector based on boosting[2] [2] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features, " In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, December 2001.

23 Features Cut detection – Software tool Webflix Camera motion[3] – Translation parameters and a zoom factor – Camera motion and close-up object motion [3] Yap-Peng Tan; Saur, D.D.; Kulkami, S.R.; Ramadge, P.J., "Rapid estimation of camera motion from compressed video with application to video annotation, " IEEE Trans. on Circuits and Systems for Video Technology, vol. 0, Feb. 2000, Page(s): 133 –146.

24 Summarization Method Shot level – Find key frames Local maxima in the face-size curve Local maxima of the camera motion Combine close key frame points as one segment – Adaptive fast playback According to visual complexity Normal playback at highlight points


26 Results



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