Recently, with the development of deep-learning, the performance of multi-object tracking algorithms based on deep neural networks has been greatly improved. However, most methods separate different functional modules into multiple networks and train them independently on specific tasks. When these network modules are used directly, they are not compatible with each other effectively, nor can they be better adapted to the multi-object tracking task, which leads to a poor tracking effect. Therefore, a network structure is designed to aggregate the regression of objects between frames and the extraction of appearance features into one model to improve the harmony between various functional modules of multi-object tracking. To improve the support for the multi-object tracking task, an end-to-end training method is also proposed to simulate the multi-object tracking process during the training and expand the training data by using the historical position of the target combined with the prediction of the motion model. A metric loss that can take advantage of the historical appearance features of the target is also used to train the extraction module of appearance features to improve the temporal correlation of extracted appearance features. Evaluation results on the MOTChallenge benchmark datasets show that the proposed approach achieves state-of-the-art performance.

近年来，随着深度学习的发展，基于深度神经网络的多目标跟踪算法的性能有了很大的提高。但是，大多数方法将不同的功能模块分成多个网络，并分别针对特定任务进行培训。当直接使用这些网络模块时，它们不能有效地相互兼容，也不能更好地适应多对象跟踪任务，从而导致跟踪效果差。因此，设计一种网络结构以将帧之间的对象回归和外观特征的提取聚合到一个模型中，以提高多对象跟踪的各个功能模块之间的协调性。为了改善对多对象跟踪任务的支持，还提出了一种端到端的训练方法，以模拟训练过程中的多目标跟踪过程，并结合目标的历史位置和运动模型的预测来扩展训练数据。可以利用目标的历史外观特征的度量损失还用于训练外观特征的提取模块，以改善提取的外观特征的时间相关性。对MOTChallenge基准数据集的评估结果表明，所提出的方法达到了最先进的性能。可以利用目标的历史外观特征的度量损失还用于训练外观特征的提取模块，以改善提取的外观特征的时间相关性。对MOTChallenge基准数据集的评估结果表明，所提出的方法达到了最先进的性能。可以利用目标的历史外观特征的度量损失还用于训练外观特征的提取模块，以改善提取的外观特征的时间相关性。对MOTChallenge基准数据集的评估结果表明，所提出的方法达到了最先进的性能。