Coupling Reranking and Structured Output SVM Co-Train for Multitarget Tracking

Most of the previous works for multitarget tracking employ two strategies: global optimization and online state estimation. In general, global methods attempt to prevent local optimization and find the best results given global models. However, in time-critical applications, global optimization has long temporal latency. In contrast, most of the online algorithms obtain the states with greedy estimation, in which the errors are hard to be corrected. In this paper, we combine these two strategies and propose an online tracking approach with short-term storage to correct some local association errors. Based on structured output support vector machine, we propose a new framework with multiple online learners to produce multiple best local linkages, and novel features based on previously generated multiframe associations are designed for reranking of these multiple linkages. The reranking also serves as the appropriate mediator for updating of the online learners by co-train algorithm. The experimental results illustrate the advantage and robustness of this reranking algorithm, and its discrimination to find optimal ones. Comparison with some state-of-the-art methods proves that our proposed method is competitive to global optimal ones and is superior to other online tracking algorithms.