Segmented trajectory based indexing and retrieval of video data

In this paper, we present a novel principal component analysis (PCA) based approach towards modeling the object trajectory in a video clip. An eigenspace decomposition of high-dimensional trajectory data leads to very compact representation, which is then used as indexing structure. To cutback on PCA computation during indexing, we first segment the trajectories into atomic subtrajectories using a curvature zero-crossing based approach followed by clustering of these subtrajectories. A two-level PCA operation with coarse-to-fine retrieval for query trajectory is then performed to generate retrieval results. Our experimental results show that our global PCA based approach performs better when input query trajectory is of similar length compared to the matching trajectories in the database. However, when partial trajectories are posed as queries our segmented trajectory based approach provides superior results for all precision-recall ratios.