Trajectory retrieval with latent semantic analysis
Proceedings of the 2008 ACM symposium on Applied computing
Technical Section: Sketch-based modeling: A survey
Computers and Graphics
Fast stroke matching by angle quantization
Proceedings of the First International Conference on Immersive Telecommunications
Experimental comparison of DWT and DFT for trajectory representation
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
International Journal of Computer Vision
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Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. In this paper we propose a novel vision system for clustering and classification of object-based video motion clips using spatiotemporal models. Object trajectories are modeled as motion time series using the lowest order Fourier coefficients obtained by Discrete Fourier Transform. Trajectory clustering is then carried out in the DFT-coefficient feature space to discover patterns of similar object motion activity. The DFT coefficients are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a simple Mahalanobis classifier for the detection of anomalous trajectories. Our proposed techniques are validated on three different datasets - Australian sign language, handlabelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for visual surveillance systems are envisaged.