smartplayer : user-centric video fast-forwarding
DESCRIPTION
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 ). Outline. Introduction SmartPlayer User-Centric Video Fast-Forwarding Skimming Model User Interface - PowerPoint PPT PresentationTRANSCRIPT
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)
Outline
• Introduction• SmartPlayer– User-Centric Video Fast-Forwarding– Skimming Model– User Interface
• Results• Conclusion
Introduction
• Microsoft Windows Media Player– Play, pause, stop, fast-forward, rewind/reverse video
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
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
Introduction
• SmartPlayer
User Behavior Observation And Inquiry
• User inquiry– 10 participants: 5 males and 5 females
– How users fast-forwarding these videos?
Video type Number of people who Fast-forward
Surveillance video 10Sport video 9Movies 0Lecture videos 2
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.
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
Skimming Model
• Speed control– motion complexity– speed of the previous content
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.
Skimming Model
• Semantic layer– Extract semantic event points in video– Manual annotation
Video type Events
Baseball Pitch, hit, homerun……
Surveillance Appearance of pedestrians, cars, bicycles
Wedding Formal wedding procedure
News Political, financial, life, international event
Drama No event
Skimming Model
• Personalization layer– Learning from user input
User Interface
Results
• Personalized adaptive fast-forwarding– 20 participants: 13 males and 7 females
Results
• Comparisons of different video players
Video content understanding rateVideo watching time
Results
• Average rating of three types of video players
Results
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
An Extended Framework for Adaptive Playback-Based Video Summarization
Kadir A. Peker and Ajay Divakaran
SPIE ITCOM 2003
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
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.
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.
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
Results
Results