Title :
Multifeature Object Trajectory Clustering for Video Analysis
Author :
Anjum, Nadeem ; Cavallaro, Andrea
Author_Institution :
Multimedia & Vision Group, Queen Mary Univ. of London, London
Abstract :
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.
Keywords :
object detection; pattern clustering; position control; video signal processing; cluster merging; clustering algorithm; common patterns; information fusion; multifeature object trajectory; nonparametric clustering; outlier recognition; rare object motion patterns; reoccurring trajectories; representative trajectory features; similar adjacent clusters; standard data-sets; video analysis; Anomaly detection; clustering; mean-shift; trajectory analysis;
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
DOI :
10.1109/TCSVT.2008.2005603