A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera
Ambient Intelligence and Smart Environments,
Volume 3,
Number 3,
pages 237--252,
doi: 10.3233/AIS-2011-0111
- Jan 2011
Associated documents :
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 persons 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.
@Article{YWW11,
author = {Yan, Wenjie and Weber, Cornelius and Wermter, Stefan},
title = {A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera},
journal = {Ambient Intelligence and Smart Environments},
number = {3},
volume = {3},
pages = {237--252},
year = {2011},
month = {Jan},
publisher = {IOS Press Amsterdam},
doi = {10.3233/AIS-2011-0111},
}