Trajectory representation using Gabor features for motion-based video retrieval
Pattern Recognition Letters
Joint trajectory tracking and recognition based on bi-directional nonlinear learning
Image and Vision Computing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Event analysis based on multiple interactive motion trajectories
IEEE Transactions on Circuits and Systems for Video Technology
A novel trajectory clustering approach for motion segmentation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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
Extracting representative motion flows for effective video retrieval
Multimedia Tools and Applications
Learning to rank biological motion trajectories
Image and Vision Computing
Recognizing jump patterns with physics-based validation in human moving trajectory
Journal of Visual Communication and Image Representation
International Journal of Computer Vision
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This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent trajectories as temporal ordering of subtrajectories. This approach solves the problem of trajectory representation when only partial trajectory information is available due to occlusion. It is achieved by a hypothesis testing-based method applied to curvature data computed from trajectories. The subtrajectories are then represented by their principal component analysis (PCA) coefficients for optimally compact representation. Different techniques are integrated to index and retrieve subtrajectories, including PCA, spectral clustering, and string matching. We assume a query by example mechanism where an example trajectory is presented to the system and the search system returns a ranked list of most similar items in the dataset. Experiments based on datasets obtained from University of California at Irvine's KDD archives and Columbia University's DVMM group demonstrate the superiority of our proposed PCA-based approaches in terms of indexing and retrieval times and precision recall ratios, when compared to other techniques in the literature