Tracking persons under partial scene occlusion using linear regression

A new approach for human tracking under partial scene occlusion is proposed in this paper. Our method employs linear regression tracking and a camera model to find the most probable person position and height in the current frame. The system is stabilized by combining a priori knowledge, linear regression tracking, and the predicted position from a Kalman filter. Experimental results show that our approach works robustly in complex scenes.