A self-organizing approach to detection of moving patterns for real-time applications

Abstract

Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science. The proposed model allows to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos taken with stationary cameras. We compared our method with other modeling techniques. Experimental results, both in terms of detection accuracy and in terms of processing speed, are presented for color video sequences which represent typical situations critical for video surveillance systems. © 2007 Springer-Verlag Berlin Heidelberg.