Preserving topological information in sub-trajectories-based representation for spatio-temporal trajectories indexing and retrieval

摘要

Trajectories of moving objects are known as one of the most important cues for understanding semantics in video data. Although there are a lot of significant researches dealing with trajectory analysis tailored to indexing and retrieval, several problems still remain. One of them is a trade-off between whole trajectory- and sub-trajectories- based methods. The former problem is that representing a trajectory as a whole is not appropriate for detecting similar patterns of the trajectory. In contrast, the latter is that even though some key portion of two trajectories share similar patterns, the whole trajectories may be totally different. Therefore, this paper proposes a novel method to optimize such trade-off. By representing a trajectory as a combination of sequence of "word" - each word's character represents one distinct feature extracted from sub-trajectories (i.e. segments), and a topological graph of trajectory's segments, the proposed method is shift and scale invariant, can handle occlusion and distortion, and can discover similar patterns among trajectories. Thorough comparisons with well-known methods demonstrate the superiority of the proposed method in terms of precision recall ratios.