Shen Jianbing, Liang Zhiyuan, Liu Jianhong, Sun Hanqiu, Shao Ling, Tao Dacheng
IEEE Trans Cybern. 2018 Feb 27. doi: 10.1109/TCYB.2018.2803217.
In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and min-cost flow, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm. Our source code is available at https://github.com/shenjianbing/submodulartrack.
在本文中，我们提出了一种基于次模优化的新型多目标视觉跟踪算法。所提出的算法由两个主要阶段组成。在第一阶段，提出了一种新的轨迹段选择策略来处理遮挡问题。我们分别使用重叠准则和最小成本流生成低级轨迹段，然后将它们整合到候选轨迹段集合中。在第二阶段，我们将多目标跟踪问题表述为受相关约束的次模最大化问题。次模函数从候选轨迹段集合中选择正确的轨迹段以形成目标轨迹。然后，我们设计了一个连接过程来连接相应的轨迹以克服遮挡问题。实验结果证明了我们跟踪算法的有效性。我们的源代码可在https://github.com/shenjianbing/submodulartrack获取。
