A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
A general method for human activity recognition in video
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Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)
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Learning semantic scene models by trajectory analysis
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Abnormal detection using interaction energy potentials
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A generalized uncertainty principle and sparse representation in pairs of bases
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Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
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Abnormal behavior detection has been one of the most important research branches in intelligent video content analysis. In this paper, we propose a novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA). Given a video scenario, we collect trajectories of normal behaviors and extract the control point features of cubic B-spline curves to construct a normal dictionary set, which is further divided into Route sets. On the dictionary set, sparse reconstruction coefficients and residuals of a test trajectory to the Route sets can be calculated with SRA. The minimal residual is used to classify the test behavior into a normal behavior or an abnormal one. SRA is solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small. Experimental results with comparisons show that the proposed approach improves the state-of-the-art.