Robust tracking of persons in real-world scenarios using a statistical computer vision approach
In the following work we present 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 within 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 e.g. panning or zooming. We consider this as a 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. Additionally, we show how our approach can be effectively extended in order to include on-line background adaptation. Our results are confirmed by several tracking examples in real scenarios, shown at the end of the article and provided on the web server of our institute.
person tracking hidden markov models kalman-filter background adaptation statistical object modeling