In [1], we proposed a graph-based formulation that links and clusters person
hypotheses over time by solving a minimum cost subgraph multicut problem. In
this paper, we modify and extend [1] in three ways: 1) We introduce a novel
local pairwise feature based on local appearance matching that is robust to
partial occlusion and camera motion. 2) We perform extensive experiments to
compare different pairwise potentials and to analyze the robustness of the
tracking formulation. 3) We consider a plain multicut problem and remove
outlying clusters from its solution. This allows us to employ an efficient
primal feasible optimization algorithm that is not applicable to the subgraph
multicut problem of [1]. Unlike the branch-and-cut algorithm used there, this
efficient algorithm used here is applicable to long videos and many detections.
Together with the novel feature, it eliminates the need for the intermediate
tracklet representation of [1]. We demonstrate the effectiveness of our overall
approach on the MOT16 benchmark [2], achieving state-of-art performance.

本文提出了一种基于图形的跟踪方法，使用新的本地成对特征，解决包含离群点的多切割问题并在 MOT16 基准测试上取得了最优效果。