Single Camera-Based Depth Estimation and Improved Continuously Adaptive Mean Shift Algorithm for Tracking Occluded Objects

This paper present a novel object tracking algorithm that can efficiently overcome the object occlusion problem by combining depth and color probability distribution information. The proposed algorithm consists of; i the depth estimation step using a color shift model CSM-based single camera, and ii the combination of depth and color probability distribution step using continuous adaptive mean shift CAMSHIFT algorithm, which is an adaptive version of the existing mean shift algorithm. In spite of the optimum object segmentation ability, the CAMSHIFT algorithm may fail in tracking if multiple occluded objects have similar colors. In order to overcome this limitation, the proposed algorithm combines depth and color probability distribution information. The experimental results show that the proposed algorithm is real time for well tracking the occluded object which cannot be tracked by the traditional CAMSHIFT algorithm, and the accuracy of depth estimation of the proposed algorithm is about 97.5i¾ź%.