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Multiple Vehicles Tracking via sequential posterior estimation  

Lee, Won-Ju (The Department of Electrical and Electronic Engineering, Yonsei University)
Yoon, Chang-Young (The Department of Electrical and Electronic Engineering, Yonsei University)
Lee, Hee-Jin (The Department of Information Control Engineering, Hankyong National University)
Kim, Eun-Tai (The Department of Electrical and Electronic Engineering, Yonsei University)
Park, Mignon (The Department of Electrical and Electronic Engineering, Yonsei University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.44, no.1, 2007 , pp. 40-49 More about this Journal
Abstract
In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be 'distracted' causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.
Keywords
Color-based tracking; Monte Carlo Filter; Multiple hypothesis; Particle Filter; Optical Flow;
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