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http://dx.doi.org/10.5391/JKIIS.2015.25.4.377

Forward Vehicle Tracking Based on Weighted Multiple Instance Learning Equipped with Particle Filter  

Park, Keunho (School of Computer Engineering and Science, Chonbuk National University)
Lee, Joonwhoan (School of Computer Engineering and Science, Chonbuk National University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.25, no.4, 2015 , pp. 377-385 More about this Journal
Abstract
This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.
Keywords
WMIL; Particle Filter; Forward Vehicle; Object Tracking; Video Frames; Haar-like Features;
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