Unsupervised online learning trajectory analysis based on weighted directed graph

Publication Type:
Conference Proceeding
Proceedings - International Conference on Pattern Recognition, 2012, pp. 1306 - 1309
Issue Date:
Filename Description Size
Thumbnail2012000972OK.pdf495.23 kB
Adobe PDF
Full metadata record
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. © 2012 ICPR Org Committee.
Please use this identifier to cite or link to this item: