A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Spatio-Temporal Motion Segmentation via Level Set Partial Differential Equation
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Activity Recognition Based on Multiple Motion Trajectories
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Multiple Object Tracking with Kernel Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences
IEEE Transactions on Multimedia
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
Signal Processing
Behavioural analysis with movement cluster model for concurrent actions
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
International Journal of Computer Vision
International Journal of Computer Vision
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Motion information is regarded as one of the most important cues for developing semantics in video data. Yet it is extremely challenging to build semantics of video clips particularly when it involves interactive motion of multiple objects. Most of the existing research has focused on capturing and modelling the motion of each object individually thus loosing interaction information. Such approaches yield low precision-recall ratios and limited indexing and retrieval performances. This paper presents a novel framework for compact representation of multi-object motion trajectories. Three efficient multi-trajectory indexing and retrieval algorithms based on multilinear algebraic representations are proposed. These include: (i) geometrical multiple-trajectory indexing and retrieval (GMIR), (ii) unfolded multiple-trajectory indexing and retrieval (UMIR), and (iii) concentrated multiple-trajectory indexing and retrieval (CMIR). The proposed tensor-based representations not only remarkably reduce the dimensionality of the indexing space but also enable the realization of fast retrieval systems. The proposed representations and algorithms can be robustly applied to both full and partial (segmented) multiple motion trajectories with varying number of objects, trajectory lengths, and sampling rates. The proposed algorithms have been implemented and evaluated using real video dataset. Simulation results demonstrate that the CMIR algorithm provides superior precision-recall metrics, and smaller query processing time compared to the other approaches.