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arXiv · 1712.05116

Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking

Longtao Chen·Jing Lou·Wei Zhu·Qingyuan Xia·Mingwu Ren
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Abstract

Aiming to address the fast multi-object tracking for dense small object in the cluster background, we review track orientated multi-hypothesis tracking(TOMHT) with consideration of batch optimization. Employing autocorrelation based motion score test and staged hypotheses merging approach, we build our homologous hypothesis generation and management method. A new one-to-many constraint is proposed and applied to tackle the track exclusions during complex occlusions. Besides, to achieve better results, we develop a multi-appearance segmentation for detection, which exploits tree-like topological information and realizes one threshold for one object. Experimental results verify the strength of our methods, indicating speed and performance advantages of our tracker.

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Longtao Chen, Jing Lou, Wei Zhu, Qingyuan Xia, Mingwu Ren. 2017-12-14. Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking. https://doi.org/10.1371/journal.pone.0206168

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