A Robust 3D Feature-Based People Stereo Tracking Algorithm

Abstract

Abstract. This paper presents a 3D feature-based people tracking algorithm which combines an interacting multiple model (IMM) algorithm with a cascade multiple feature data association algorithm. The IMM algorithm in this paper only uses an adaptive Kalman Filter and two dynamic models consisting of a constant velocity model (CV) and a current statistics model (CS) to predict the 3D location of people maneuvering and update the prediction with corresponding measurement. The cascade multiple feature data association algorithm in this paper utilizes three hypotheses, including the nearest distance hypothesis, the velocity consistency hypothesis, and the intensity consistency hypothesis, in turn to determine which trajectory a measurement should be assigned to. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.