Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Video Behaviour Profiling and Abnormality Detection without Manual Labelling
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Models for Optical Flow Analysis in Crowds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Towards Generic Detection of Unusual Events in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
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Detection of aberration in video surveillance is an important task for public safety. This paper puts forward a simple but effective framework to detect aberrations in video streams using Entropy, which is estimated on the statistical treatments of the spatiotemporal information of a set of interest points within a region of interest by measuring their degree of randomness of both directions and displacements. Entropy is a measure of the disorder/randomness in video frame. It has been showed that degree of randomness of the directions (circular variance) changes markedly in abnormal state of affairs and does change only direction variation but does not change with displacement variation of the interest point. Degree of randomness of the displacements has been put in for to counterbalance this deficiency. Simple simulations have been exercised to see the characteristics of these crude elements of entropy. Normalized entropy measure provides the knowledge of the state of anomalousness. Experiments have been conducted on various real world video datasets. Both simulation and experimental results report that entropy measures of the frames over time is an outstanding way to characterize anomalies in videos.