With the recent advances in the object detection research field, tracking-by-detection has become the leading paradigm adopted by multi-object tracking algorithms. By extracting different features from detected objects, those algorithms can estimate the similarities and association patterns of objects along with successive frames. However, since similarity functions applied by tracking algorithms are handcrafted, it is difficult to use them in new contexts. In this study, it is investigated the use of artificial neural networks to learning a similarity function that can be used among detections. During training, multilayer perceptron (MLP) neural networks were introduced to correct and incorrect association patterns, sampled from a pedestrian tracking data set. For such, different motion and appearance feature combinations have been explored. Finally, a trained MLP has been inserted into a multiple-object tracking framework, which has been assessed on the MOT Challenge benchmark. Throughout the experiments, the proposed tracker matched the results obtained by state-of-the-art methods by scoring a tracking accuracy of 60.4%, while running 58% faster than DeepSORT, a recent and similar method used as a baseline. After all, this work demonstrates its method can be automatically trained for different tracking contexts and it has highly competitive cost-effectiveness for online real-time tracking applications.

随着对象检测研究领域的最新进展，逐检测跟踪已成为多对象跟踪算法所采用的主要范例。通过从检测到的对象中提取不同的特征，这些算法可以估计对象的相似性和关联模式以及连续的帧。但是，由于跟踪算法应用的相似性函数是手工制作的，因此很难在新的上下文中使用它们。在这项研究中，研究了使用人工神经网络来学习可在检测之间使用的相似性函数。在训练过程中，引入了多层感知器（MLP）神经网络来纠正和纠正从行人跟踪数据集中采样的关联模式。为此，已经探索了不同的运动和外观特征组合。最后，将经过培训的MLP插入到多对象跟踪框架中，该框架已在MOT Challenge基准上进行了评估。在整个实验过程中，拟议的跟踪器通过跟踪精度达到60.4％，与通过最新方法获得的结果相匹配，而运行速度比DeepSORT（一种最新且类似的基准）高58％。毕竟，这项工作证明了它的方法可以针对不同的跟踪环境自动进行训练，并且对于在线实时跟踪应用程序具有极具竞争力的成本效益。而运行速度则比DeepSORT（一种最新的类似方法，用作基准）快58％。毕竟，这项工作证明了它的方法可以针对不同的跟踪环境自动进行训练，并且对于在线实时跟踪应用程序具有极具竞争力的成本效益。而运行速度则比DeepSORT（一种最新的类似方法，用作基准）快58％。毕竟，这项工作证明了它的方法可以针对不同的跟踪环境自动进行训练，并且对于在线实时跟踪应用程序具有极具竞争力的成本效益。