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 10

Sport video 9

Movies 0

Lecture 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