Disparity Weighted Histogram-Based Object Tracking for Mobile Robot Systems

Abstract

A vision-based real-time human detection and tracking capability is one of the key components of surveillance systems, human computer interfaces and monitoring systems. In this paper, we propose a method which uses color and disparity information obtained with a stereo camera. In order to achieve optimal performance with respect to detection or tracking of objects, it is better to consider multiple features together. We have developed a tracking method in which color and disparity information can be combined in a histogram. We used skin color and disparity distribution information to distinguish between different people. For human tracking, we propose a color histogram that is weighted by the disparity distribution of the target. The proposed method is simple and robust for moving camera environments and overcomes the drawbacks of conventional color histogram-based tracking methods. Experimental results show the robustness of the proposed method in environments with changing backgrounds and the tracking capabilities of targets which have similar color distributions as backgrounds or other targets. The proposed method can be used in real-time mobile robot applications.