A Particle Filter Framework for Object Tracking Using Visual-Saliency Information

摘要: Automated processing of video streams is core to current surveillance systems. The basic building blocks techniques are object detection and tracking. Tracking results further analyzed detect various events activities for situation assessment. Several approaches tracking based on background modeling. These generally vulnerable noise, illumination changes etc. Further, the may not look similar in an image sequence over time due orientation, lighting, occlusion, In this chapter, we explore application neurobiology-saliency using particle filters. We use low-level features such as color, luminance edge information along with motion cues track a single person. Experimental show that approach invariant can persons varying lighting conditions.