View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Symbolic representation and retrieval of moving object trajectories
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Weighted walkthroughs between extended entities for retrieval by spatial arrangement
IEEE Transactions on Multimedia
Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences
IEEE Transactions on Multimedia
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multifeature Object Trajectory Clustering for Video Analysis
IEEE Transactions on Circuits and Systems for Video Technology
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Trajectories of moving objects are known as one of the most important cues for understanding semantics in video data. Although there are a lot of significant researches dealing with trajectory analysis tailored to indexing and retrieval, several problems still remain. One of them is a trade-off between whole trajectory- and sub-trajectories- based methods. The former problem is that representing a trajectory as a whole is not appropriate for detecting similar patterns of the trajectory. In contrast, the latter is that even though some key portion of two trajectories share similar patterns, the whole trajectories may be totally different. Therefore, this paper proposes a novel method to optimize such trade-off. By representing a trajectory as a combination of sequence of "word" - each word's character represents one distinct feature extracted from sub-trajectories (i.e. segments), and a topological graph of trajectory's segments, the proposed method is shift and scale invariant, can handle occlusion and distortion, and can discover similar patterns among trajectories. Thorough comparisons with well-known methods demonstrate the superiority of the proposed method in terms of precision recall ratios.