A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera

Article type: Research Article

Authors: Yan, Wenjie | Weber, Cornelius | Wermter, Stefan

Affiliations: University of Hamburg, Department of Informatics, Knowledge Technology, Vogt-Kölln-Straße 30, D - 22527 Hamburg, Germany

Note: [] Corresponding author. E-mail: [email protected].

Abstract: Person tracking is an important topic in ambient living systems as well as in computer vision. In particular, detecting a person from a ceiling-mounted camera is a challenge since the person's appearance is very different from the top or from the side view, and the shape of the person changes significantly when moving around the room. This article presents a novel approach for a real-time person tracking system based on particle filters with input from different visual streams. A new architecture is developed that integrates different vision streams by means of a Sigma-Pi-like network. Moreover, a short-term memory mechanism is modeled to enhance the robustness of the tracking system. Based on this architecture, the system can start localizing a person with several cues and learn the features of other cues online. The experimental results show that robust real-time person tracking can be achieved.

Keywords: Person detection, person recognition, particle filter, neural network

DOI: 10.3233/AIS-2011-0111

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 3, no. 3, pp. 237-252, 2011