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.
@article{arxiv.1712.05116,
title = {Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking},
author = {Longtao Chen and Jing Lou and Wei Zhu and Qingyuan Xia and Mingwu Ren},
journal= {arXiv preprint arXiv:1712.05116},
year = {2020}
}