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http://dx.doi.org/10.5302/J.ICROS.2016.15.0207

Fusion of Local and Global Detectors for PHD Filter-Based Multi-Object Tracking  

Yoon, Ju Hong (Korea Electronics Technology Institute)
Hwang, Youngbae (Korea Electronics Technology Institute)
Choi, Byeongho (Korea Electronics Technology Institute)
Yoon, Kuk-Jin (Gwangju Institute of Science and Technology)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.22, no.9, 2016 , pp. 773-777 More about this Journal
Abstract
In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.
Keywords
probability hypothesis density filter; multi-object tracking; observation fusion; pedestrian tracking; random finite set theory;
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Times Cited By KSCI : 2  (Citation Analysis)
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