Robust person tracking in real scenarios with non-stationary background using a statistical computer vision approach

Abstract

This paper presents a novel approach to robust and flexible person tracking using an algorithm that combines two powerful stochastic modeling techniques: The first one is the technique of so-called Pseudo-2D Hidden Markov Models (P2DHMMs) used for capturing the shape of a person with an image frame, and the second technique is the well-known Kalman-filtering algorithm, that uses the output of the P2DHMM for tracking the person by estimation of a bounding box trajectory indicating the location of the person within the entire video sequence. Both algorithms are cooperating together in an optimal way, and with this cooperative feedback, the proposed approach even makes the tracking of persons possible in the presence of background motions, for instance caused by moving objects such as cars, or by camera operations as, for example, panning or zooming. We consider this as major advantage compared to most other tracking algorithms that are mostly not capable of dealing with background motion. Furthermore, the person to be tracked is not required to wear special equipment (e.g. sensors) or special clothing. We therefore believe that our proposed algorithm is among the first approaches capable of handling such a complex tracking problem. Our results are confirmed by several tracking examples in real scenarios, shown at the end of the paper and provided on the web server of our institute.