Unsupervised Online Learning Trajectory Analysis Based on Weighted Directed Graph

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2012 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 1306 - 1309
Issue Date:
2012-01
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In this paper, we propose a novel unsupervised online learning trajectory analysis method based on weighted directed graph. Each trajectory can be represented as a sequence of key points. In the training stage, unsupervised expectation-maximization algorithm (EM) is applied for training data to cluster key points. Each class is a Gaussian distribution. It is considered as a node of the graph. According to the classification of key points, we can build a weighted directed graph to represent the trajectory network in the scene. Each path is a category of trajectories. In the test stage, we adopt online EM algorithm to classify trajectories and update the graph. In the experiments, we test our approach and obtain a good performance compared with state-of-the-art approaches.
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