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Traffic Object Tracking Based on an Adaptive Fusion Framework for Discriminative Attributes  

Kim Sam-Yong (Dept. of Electronics and Electrical Engineering, Pohang University of Science and Technology)
Oh Se-Young (Dept. of Electronics and Electrical Engineering, Pohang University of Science and Technology)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.43, no.5, 2006 , pp. 1-9 More about this Journal
Abstract
Because most applications of vision-based object tracking demonstrate satisfactory operations only under very constrained environments that have simplifying assumptions or specific visual attributes, these approaches can't track target objects for the highly variable, unstructured, and dynamic environments like a traffic scene. An adaptive fusion framework is essential that takes advantage of the richness of visual information such as color, appearance shape and so on, especially at cluttered and dynamically changing scenes with partial occlusion[1]. This paper develops a particle filter based adaptive fusion framework and improves the robustness and adaptation of this framework by adding a new distinctive visual attribute, an image feature descriptor using SIFT (Scale Invariant Feature Transform)[2] and adding an automatic teaming scheme of the SIFT feature library according to viewpoint, illumination, and background change. The proposed algorithm is applied to track various traffic objects like vehicles, pedestrians, and bikes in a driver assistance system as an important component of the Intelligent Transportation System.
Keywords
Object tracking; visual attribute; particle filter; SIFT; driver assistance system;
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