MM '09 Proceedings of the 17th ACM international conference on Multimedia
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
Trajectory-based handball video understanding
Proceedings of the ACM International Conference on Image and Video Retrieval
Object trajectory clustering via tensor analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Anomalous video event detection using spatiotemporal context
Computer Vision and Image Understanding
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Unsupervised video surveillance
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Annotated free-hand sketches for video retrieval using object semantics and motion
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
Learning to rank biological motion trajectories
Image and Vision Computing
TODMIS: mining communities from trajectories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Moving object detection using Markov Random Field and Distributed Differential Evolution
Applied Soft Computing
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We present a novel multifeature video object trajectory clustering algorithm that estimates common patterns of behaviors and isolates outliers. The proposed algorithm is based on four main steps, namely the extraction of a set of representative trajectory features, non-parametric clustering, cluster merging and information fusion for the identification of normal and rare object motion patterns. First we transform the trajectories into a set of feature spaces on which mean-shift identifies the modes and the corresponding clusters. Furthermore, a merging procedure is devised to refine these results by combining similar adjacent clusters. The final common patterns are estimated by fusing the clustering results across all feature spaces. Clusters corresponding to reoccurring trajectories are considered as normal, whereas sparse trajectories are associated to abnormal and rare events. The performance of the proposed algorithm is evaluated on standard data-sets and compared with state-of-the-art techniques. Experimental results show that the proposed approach outperforms state-of-the-art algorithms both in terms of accuracy and robustness in discovering common patterns in video as well as in recognizing outliers.