Accurate fusion of robot, camera and wireless sensors for surveillance applications

Abstract

Often within the field of tracking people within only fixed cameras are used. This can mean that when the the illumination of the image changes or object occlusion occurs, the tracking can fail. We propose an approach that uses three simultaneous separate sensors. The fixed surveillance cameras track objects of interest cross camera through incrementally learning relationships between regions on the image. Cameras and laser rangefinder sensors onboard robots also provide an estimate of the person. Moreover, the signal strength of mobile devices carried by the person can be used to estimate his position. The estimate from all these sources are then combined using data fusion to provide an increase in performance. We present results of the fixed camera based tracking operating in real time on a large outdoor environment of over 20 non-overlapping cameras. Moreover, the tracking algorithms for robots and wireless nodes are described. A decentralized data fusion algorithm for combining all these information is presented.