Atlantis Press Paper Details: Active Tracking Using Kernel-Based Vision Processor and Robust Fuzzy Control

In this paper we introduce a practicable system by combining a vision processing algorithm and a fuzzy controller to obtain an efficient active tracker. Target tracking performance is heavily dependent on a good blend of vision algorithm and control. Because of performance and computational complexity, in practice, many visual tracking algorithms cannot be linked with control systems to track objects in real-time. Robustness and speed are the two major bottlenecks of current visual tracking algorithms. In this paper, the target's visual model is used along with a kernel-based searching algorithm to predict the target position. A model update is also incorporated to recognize when the target’s appearance is changing due to its pose change. In case of target track loss, a search algorithm sweeps the space in vicinity of last target position to recover the lost target. A motion detection module used in our tracking system not only helps to initiate the tracking process automatically, it also finds the target’s presence after track loss. A fuzzy control is also synthesized to reach our control performance objectives. The idea is implemented in an active camera system to track moving targets. In addition, we've used the parallel processing technique for vision and control to reach acceptable speed and accuracy in real-time tracking.

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title:		Active Tracking Using Kernel-Based Vision Processor and Robust Fuzzy Control
publication:		JCIS-2006 Proceedings
part of series:		Advances in Intelligent Systems Research
ISBN:		978-90-78677-01-7
ISSN:		1951-6851
DOI:		doi:10.2991/jcis.2006.276 (how to use a DOI)
author(s):		Alireza Mustafa, Moteaal Asadi Shirzi, Muhammad Reza Hairi Yazdi
corresponding author:		Alireza Mustafa
publication date:		October 2006
keywords:		Tracking, Fuzzy Control, Mean Shift Algorithm, Kernel Based Tracking, Parallel Processing
abstract:		In this paper we introduce a practicable system by combining a vision processing algorithm and a fuzzy controller to obtain an efficient active tracker. Target tracking performance is heavily dependent on a good blend of vision algorithm and control. Because of performance and computational complexity, in practice, many visual tracking algorithms cannot be linked with control systems to track objects in real-time. Robustness and speed are the two major bottlenecks of current visual tracking algorithms. In this paper, the target's visual model is used along with a kernel-based searching algorithm to predict the target position. A model update is also incorporated to recognize when the target’s appearance is changing due to its pose change. In case of target track loss, a search algorithm sweeps the space in vicinity of last target position to recover the lost target. A motion detection module used in our tracking system not only helps to initiate the tracking process automatically, it also finds the target’s presence after track loss. A fuzzy control is also synthesized to reach our control performance objectives. The idea is implemented in an active camera system to track moving targets. In addition, we've used the parallel processing technique for vision and control to reach acceptable speed and accuracy in real-time tracking.
copyright:		© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:		JCIS06-FTT-2.pdf (2979 K)