With the advancement of video analysis technology, the multi-object tracking (MOT) problem in complex scenes involving pedestrians is gaining increasing importance. This challenge primarily involves two key tasks: pedestrian detection and re-identification. While significant progress has been achieved in pedestrian detection tasks in recent years, enhancing the effectiveness of re-identification tasks remains a persistent challenge. This difficulty arises from the large total number of pedestrian samples in multi-object tracking datasets and the scarcity of individual instance samples. Motivated by recent rapid advancements in meta-learning techniques, we introduce MAML MOT, a meta-learning-based training approach for multi-object tracking. This approach leverages the rapid learning capability of meta-learning to tackle the issue of sample scarcity in pedestrian re-identification tasks, aiming to improve the model's generalization performance and robustness. Experimental results demonstrate that the proposed method achieves high accuracy on mainstream datasets in the MOT Challenge. This offers new perspectives and solutions for research in the field of pedestrian multi-object tracking.

翻译：随着视频分析技术的进步，涉及行人的复杂场景中的多目标跟踪问题日益重要。这一挑战主要涉及两个关键任务：行人检测与重识别。近年来，尽管行人检测任务已取得显著进展，但提升重识别任务的有效性仍是一个持续存在的难题。该困难源于多目标跟踪数据集中行人样本总数庞大而单个实例样本稀缺。受元学习技术近期快速发展的启发，我们提出了MAML MOT，一种基于元学习的多目标跟踪训练方法。该方法利用元学习的快速学习能力，以应对行人重识别任务中的样本稀缺问题，旨在提升模型的泛化性能与鲁棒性。实验结果表明，所提方法在MOT Challenge主流数据集上实现了较高的准确率。这为行人多目标跟踪领域的研究提供了新的视角与解决方案。