Trajectory Matching and Classification of Video Moving Objects

A top-to-bottom hierarchical clustering algorithm to classify trajectories is proposed, and several experiments demonstrate that the schemes are efficient in matching and classifying different shape and direction trajectories.

Abstract

Trajectory matching is an important way to describe and classify behaviors of moving objects in a computer visual system. In this paper, we present two trajectory description methods, time-sampling sequence and space-sampling sequence, which can be used in different matching applications. We then propose two general trajectory matching schemes based on Levenshtein distance and relaxation matching respectively. Trajectory Levenshtein distance scheme is a good way to compare the topological shapes and directions of trajectories, and can be performed quickly. Trajectory relaxation matching scheme can gain the statistical optimal matching. Finally, we propose a top-to-bottom hierarchical clustering algorithm to classify trajectories, and several experiments demonstrate that our schemes are efficient in matching and classifying different shape and direction trajectories