Person Tracking in Real-World Scenarios Using Statistical Methods

Gerhard Rigoll, Stefan Eickeler, Stefan Müller

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 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 people possible in the presence of background motions caused by moving objects or by camera operations as e.g. panning or zooming. 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.