IEEE Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), October 2003 Spatiotemporal Blocks-Based Moving Objects Identification and Tracking

In this paper we propose a new representation of videos with spatiotemporal blocks. After a given video is decomposed into the spatiotemporal blocks, a dimensionality reduction technique is applied to obtain a compact vector representation of each block gray level values. The block vectors provide a joint representation of texture and motion patterns in videos. Our results on PETS repository videos show that detection and tracking of moving objects is substantially improved if based on spatiotemporal blocks instead on pixels. Thus, we go away from the standard input of pixel values that are known to be noisy and the main cause of instability of video analysis algorithms.