A hierarchical self-organizing approach for learning the patterns of motion trajectories

IEEE Transactions on Neural Networks
Weiming HuTieniu Tan

Abstract

The understanding and description of object behaviors is a hot topic in computer vision. Trajectory analysis is one of the basic problems in behavior understanding, and the learning of trajectory patterns that can be used to detect anomalies and predict object trajectories is an interesting and important problem in trajectory analysis. In this paper, we present a hierarchical self-organizing neural network model and its application to the learning of trajectory distribution patterns for event recognition. The distribution patterns of trajectories are learnt using a hierarchical self-organizing neural network. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Compared with the existing neural network structures that are used to learn patterns of trajectories, our network structure has smaller scale and faster learning speed, and is thus more effective. Experimental results using two different sets of data demonstrate the accuracy and speed of our hierarchical self-organizing neural network in learning the distribution patterns of object trajectories.

References

Feb 6, 2008·IEEE Transactions on Neural Networks·M C Su, H T Chang

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Citations

Dec 14, 2005·IEEE Transactions on Neural Networks·Sitao Wu, Tommy W S Chow
Mar 23, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Kaiqi HuangTieniu Tan
Oct 16, 2004·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Weiming HuSteve Maybank

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