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
Correspondence-Free Activity Analysis and Scene Modeling in Multiple Camera Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
Multicamera video summarization and anomaly detection from activity motifs
ACM Transactions on Sensor Networks (TOSN)
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We propose a principled approach to video summarization using optimal reconstruction as a metric to guide the creation of the summary output. The spatio-temporal video patches included in the summary are viewed as observations about the local motion of the original input video and are chosen to minimize the reconstruction error of the missing observations under a set of learned predictive models. The method is demonstrated using fixed-viewpoint video sequences and shown to generalize to multiple camera systems with disjoint views, which can share activity already summarized in one view to inform the summary of another. The results show that this approach can significantly reduce or even eliminate the inclusion of patches in the summary that contain activities from the video that are already expected based on other summary patches, leading to a more concise output.