Moving Object Segmentation by Length-Unconstrained Trajectory Analysis.

Qiyu Liao, Bingbing Zhuang, Jingjing Wang, Nenghai Yu

Abstract: Background subtraction for moving cameras is an unsolved key problem in intelligent video analysis. Trajectory analysis has demonstrated a significant difference between background and foreground motion model. But under limitation of trajectory-tracking technique, long-term trajectories are hardly dense and well distributed enough, which may cause inaccuracy in boundary discrimination. Addressed to these problems, in this paper we proposed a robust algorithm of “length unconstrained trajectory analysis” (LUCTA), to recapture “invalided” information of short trajectories. Extensive experiments demonstrate competitive performance of our frame work on both accuracy and time cost.