The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A master-slave system to acquire biometric imagery of humans at distance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Hidden Markov Models for Optical Flow Analysis in Crowds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Detecting irregularity in videos using kernel estimation and KD trees
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Activity based surveillance video content modelling
Pattern Recognition
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
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
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper presents a novel unusual behaviors detection algorithm to acquire biometric data for intelligent surveillance in real-time. Our work aims to design a completely unsupervised method for detecting unusual behaviors without using any explicit training dataset. To this end, the proposed approach learns from the behaviors recorded in the history; such that the definition of unusual behavior is modeled according to previous observations, but not a manually labeled dataset. To implement this, pyramidal Lucas-Kanade algorithm is employed to estimate the optical flow between consecutive frames, the results are encoded into flow histograms. Leveraging the correlations between the flow histograms, unusual actions can be detected by applying principal component analysis (PCA). This approach is evaluated under both indoor and outdoor surveillance scenarios. It shows promising results that our detection algorithm is able to discover unusual behaviors and adapt to changes in behavioral pattern automatically.