Improved person tracking using a combined pseudo-2D-HMM and Kalman filter approach with automatic background state adaptation

This paper presents the continuation of our work on object tracking in presence of non-stationary background using a combination of a pseudo-2D hidden Markov model (P2DHMM) and a Kalman filter. It presents a major improvement by introducing a novel method that allows an automatic adaptation of our system to the changing background. Other improvements of the system´s tracking capabilities are achieved by refined person models and normalization procedures. One of the major goals of our approach to tracking is to achieve high quality tracking results despite non-stationary background that can be caused e.g. by moving objects in the background or by camera operations such as panning or zooming. In previous publications we demonstrated that our combined P2DHMM/Kalman filter approach is an interesting solution to this problem, because it enables us to perform person tracking without the use of motion information. In this paper, we show that this approach can be further improved by adapting the system to the constantly changing background. We further demonstrate that such a background adaptation is very difficult to achieve in standard tracking approaches but can be effectively realized in our combined P2DHMM/Kalman filter approach. The effectiveness of this new procedure is demonstrated in experiments, where the tracking results and the quality of the person segmentation of our original system is compared to the results obtained with the improved approach