Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers. In order to handle this problem, we propose an Approximation-Shrink Scheme for sequential optimization. This scheme is realized by introducing an Ambiguity-Clearness Graph to avoid conflicts and maintain sequence independent, as well as a sliding window optimization framework to constrain the size of state space and guarantee convergence. Based on this window-wise framework, the states of targets are clustered in a self-organizing manner. Moreover, we show that the traditional online and batch tracking methods can be embraced by the window-wise framework. Experiments indicate that with only a small window, the optimization performance can be much better than online methods and approach to batch methods.
@article{arxiv.1511.08913,
title = {Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking},
author = {Qi Guo and Le Dan and Dong Yin and Xiangyang Ji},
journal= {arXiv preprint arXiv:1511.08913},
year = {2015}
}