Making a Long Video Short: Dynamic Video Synopsis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-camera calibration, object tracking and query generation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 02
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Personalized and automatic social summarization of events in video
Proceedings of the 16th international conference on Intelligent user interfaces
Personalized video summarization with human in the loop
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Stream-based active unusual event detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Multicamera video summarization from optimal reconstruction
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Video summarization using a self-growing and self-organized neural gas network
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
VISON: VIdeo Summarization for ONline applications
Pattern Recognition Letters
Automatic consumer video summarization by audio and visual analysis
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Multi-View Video Summarization
IEEE Transactions on Multimedia
Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
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Camera network systems generate large volumes of potentially useful data, but extracting value from multiple, related videos can be a daunting task for a human reviewer. Multicamera video summarization seeks to make this task more tractable by generating a reduced set of output summary videos that concisely capture important portions of the input set. We present a system that approaches summarization at the level of detected activity motifs and shortens the input videos by compacting the representation of individual activities. Additionally, redundancy is removed across camera views by omitting from the summary activity occurrences that can be predicted by other occurrences. The system also detects anomalous events within a unified framework and can highlight them in the summary. Our contributions are a method for selecting useful parts of an activity to present to a viewer using activity motifs and a novel framework to score the importance of activity occurrences and allow transfer of importance between temporally related activities without solving the correspondence problem. We provide summarization results for a two camera network, an eleven camera network, and data from PETS 2001. We also include results from Amazon Mechanical Turk human experiments to evaluate how our visualization decisions affect task performance.