Abstract: In response to problems such as missed detections and unfriendly data association algorithms leading to frequent switching among objects in complex environments, a multi-object tracking algorithm MFMOT that utilizes the FairMOT framework as its foundation is proposed. Firstly, a lightweight multi-branch attention module is designed, which utilizes channel grouping to reduce complexity and enhances features from three dimensions, enabling the network to select and extract feature information. Secondly, the re-identification branch uses the PolyLoss loss function to enhance the semantic information between similar objects to distinguish different objects of the same type. Finally, a multi-feature fusion similarity matrix is proposed to obtain the optimal similarity matrix by fusing multiple feature similarity matrices, reducing the number of identity switches between targets. The experimental results show that the HOTA scores are 61.5% and 56.1% in the MOT17 and MOT20 datasets respectively, which improves by 2.2 percentage points and 2.3 percentage points compared to the original FairMOT model. Furthermore, when applying the multi-feature fusion similarity matrix to a multi-object tracking method with the same mode as FairMOT, improvements in HOTA, MOTA, and IDF1 are observed.

Key words: multi-object tracking, re-identification, attention mechanism, similarity matrix

摘要： 针对复杂环境下多目标跟踪出现漏检与数据关联算法不友好导致目标之间频繁发生切换等问题，提出了基于FairMOT框架的多目标跟踪算法MFMOT。设计了轻量化多支路注意力模块，利用通道分组降低复杂度，从三个维度进行特征增强，使网络筛选提取到特征信息。重识别分支采用PolyLoss损失函数，增强同类目标之间的语义信息以区分同类不同目标对象。提出多特征融合相似度矩阵，通过融合多种特征相似度矩阵得到最优的相似度矩阵，降低目标之间身份切换次数。实验结果表明，在MOT17与MOT20数据集中HOTA分别为61.5%与56.1%，相比原有FairMOT模型分别提升2.2个百分点与2.3个百分点。与此同时，将多特征融合相似度矩阵应用至与FairMOT相同模式的多目标跟踪方法中，HOTA、MOTA与IDF1均得到提升。

关键词: 多目标跟踪, 重识别, 注意力机制, 相似度矩阵