‘Bag of segments’ for motion trajectory analysis
Yue Zhou, Thomas S. Huang · 2008
We propose a novel method to represent motion trajectory - ‘bag of segments’. Motivated by the ‘bag of words’ approach in text mining and more recently in image categorization, we represent each trajectory as a collection of segments, each assigned a membership to a codeword of a dictionary. The trajectory segments are represented by their shape and motion information while the inter-segment relationship is ignored. In the ’bag of trajectory segments’ representation, the trajectories are transformed to a vector in the codeword space. The codebook is generated by clustering a large amount of trajectory segments using Expectation Maximization (EM) algorithm and each codeword is a weighted regression model. Given the codebook, trajectories are segmented and associated with a soft-frequency vector in codeword space as an intermediate representation. With the ‘bag of segments’ representation, trajectory analysis can be carried out in vector space as traditional data. Experiments show the ‘bag of segments’ method is very effective in trajectory similarity search and classification.