Hardware Acceleration of Hidden Markov Model Decoding for Person Detection
Proceedings of the conference on Design, Automation and Test in Europe - Volume 3
A neural approach to extract foreground from human movement images
Computer Methods and Programs in Biomedicine
Moving vehicle tracking for the web camera
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Toward visually inferring the underlying causal mechanism in a traffic-light-controlled crossroads
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Hi-index | 0.00 |
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.