A motion trajectory based video retrieval system using parallel adaptive self organizing maps

Abstract

We present a novel motion trajectory based video retrieval system using LAMSTAR-based adaptive self organizing maps (PASOMs) in this paper. The trajectories are extracted from video by a robust tracker. To reduce the high dimension of motion trajectories, we first decompose each trajectory into sub-trajectories by using a maximum acceleration based approach. Each subtrajectory is then modeled and coded by two different methods, polynomial curving fitting and independent component analysis. To fuse the different features of subtrajectories for more efficient and flexible retrieval, we use PASOMs as the searching tool. Experimental results show the superior performance of the proposed approach for video retrieval comparing with prior approaches.