Marker-less Stereo-Vision Human Motion Tracking Using Hybrid Filter in Unconstrained Environment

Abstract

Stereo-vision technology has shown its advantages to overcome the occlusion and realistic information. However, marker-less human motion detection and tracking in the unconstrained environment were led to the difficulty of features extraction. In this paper, we proposed a hybrid technique of Gaussian and median filter to improve the shadow and sudden change of the illumination problems. The skeleton model of the detected human was constructed using the sequential mathematical morphology. Based on the results, the skeleton model produced was not affected by the shadow and the illumination issue. Proposed approach and the normalized filter approach produces up to 86% and 71% of the average accuracy tracking respectively in the real-time tracking. Hence, the proposed approach could improve the performance of the human detection in the unconstrained environment.