全局多极团的分层关联多目标跟踪

目的 由于背景的复杂性，光照的多变性以及目标的相关性等因素的影响，使得多目标跟踪算法的鲁棒性相对较差。目前，在多目标跟踪问题中面临的主要挑战包括：遮挡、误检、目标运动的复杂性以及由于目标具有相似的外观特征所引起的模糊性。针对以上问题，提出一种基于全局多极团的分层关联多目标跟踪算法。方法 该方法以数据关联中的全局关联为依托，基于分层和网络流思想，跟踪采用两层框架，每一层中均利用较短的轨迹片段形成更长的轨迹，根据网络流思想，首先构建网络的无向图，其中无向图的结点是由几个轨迹片段构成的，无向图权值的确定是利用目标的运动模型和外观模型的线性组合得到，然后借助聚合虚拟结点处理目标之间的遮挡问题，接着重点加入空间约束以解决身份转换的问题。最后利用最大二值整数规划在叠加片段上求解无向图，同时得到多个极大团。结果 实验在公共数据集上进行，通过在TUD-Stadmitte、TUD-Crossing、PETS2009、Parking Lot 1、Parking Lot 2、Town Center这6个数据集上验证，该方法对各个数据集跟踪准确度均有提高，其中针对数据集TUD-Stadmitte提高了5%以上，针对数据集Town Center处理的身份转换数量减少了12个。结论 本文依据数据关联思想，提出一种全局多极团的分层关联多目标跟踪算法，其中重点加入的空间约束能有效地处理多目标跟踪问题，尤其涉及遮挡问题，效果更佳。在智能视频监控领域中该方法具有实际应用价值。

Hierarchical multi-object tracking algorithm based on globally multiple maximum clique graphs

Objective The variability of the illumination and the correlation of the object,the robustness of multi-object tracking algorithm is relatively poor because of the complexity of the background.The main challenges in multi-object tracking include occlusion,false positives,complexity of object motion,and ambiguity caused by similar features in appearance.To solve the above problems,a hierarchical multi-object tracking algorithm based on globally multiple maximum clique graphs is proposed.Method The method is based on global association of data association,hierarchical,and network flow theory and uses a two-layer framework.Each layer uses a shorter trajectory to form a longer trajectory.An undirected graph is first constructed according to the network flow theory.The nodes are composed of several track segments and the weights are obtained using the linear combination of the motion model and the appearance model of the object.Then,the occlusion object is processed by the aggregation dummy node,and the spatial constraint is added to solve the problem of identity transformation.Finally,the mixed-binary-integer programming is used to solve the undirected graphs on the superposition problem.Simultaneously,a plurality of maximal cliques is obtained.Result Experiments are conducted on public datasets through TUD-Stadmitte,TUD-Crossing,PETS2009,Parking Lot 1,Parking Lot 2,and Town Center to verify the method,and all the datasets show desirable results.The number of identity transformation handled by the dataset Town Center is 12,and is higher by more than 5% for the dataset TUD-Stadmitte.Conclusion Based on the idea of data association,this paper proposes a hierarchical multi-object tracking algorithm based on globally multiple maximum clique graphs.The spatial constraint of key can effectively deal with multi-object tracking problem,especially the problem of occlusion effect.This method has practical application value in intelligent video surveillance.