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Unsupervised online learning trajectory analysis based on weighted directed graph

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3 Author(s)
Yuan Shen ; Inst. of Inf. Sci., Beijing Jiaotong Univ., Beijing, China ; Zhenjiang Miao ; Jian Zhang

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.

Published in:

Pattern Recognition (ICPR), 2012 21st International Conference on

Date of Conference:

11-15 Nov. 2012

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