Upper Body Tracking Using KLT and Kalman Filter

Human monitoring system based on image and sequence analysis is employed in security surveillance systems. For this purpose upper-body tracking is often needed. However, tracking challenges such as variations and similarity in appearance can mislead limbs tracker system. This paper describes a novel framework for visual tracking of human upper body parts in an indoor environment which can handle the tracking challenges. It is based on Kanade-Lucas-Tomasi (KLT) and motion model Kalman filter approach. In our approach, different upper body limbs are tracked by the KLT methods and then the motion model is imposed to the Kalman filter to predict and estimate the best tracked patch of KLT tracking results. These characteristics make our approach suitable for visual surveillance applications. Experiments on different datasets show the effciency of our approach on tracking the human upper body limbs.