Continual learning allows a model to learn multiple tasks sequentially while retaining the old knowledge without the training data of the preceding tasks. This paper extends the scope of continual learning research to class-incremental learning for \ac{mot}, which is desirable to accommodate the continuously evolving needs of autonomous systems. Previous solutions for continual learning of object detectors do not address the data association stage of appearance-based trackers, leading to catastrophic forgetting of previous classes' re-identification features. We introduce COOLer, a COntrastive- and cOntinual-Learning-based tracker, which incrementally learns to track new categories while preserving past knowledge by training on a combination of currently available ground truth labels and pseudo-labels generated by the past tracker. To further exacerbate the disentanglement of instance representations, we introduce a novel contrastive class-incremental instance representation learning technique. Finally, we propose a practical evaluation protocol for continual learning for MOT and conduct experiments on the \bdd and \shift datasets. Experimental results demonstrate that COOLer continually learns while effectively addressing catastrophic forgetting of both tracking and detection. The code is available at \url{https://github.com/BoSmallEar/COOLer}.

翻译：持续学习使模型能够顺序学习多个任务，同时保留旧知识而无需先前任务的训练数据。本文将持续学习研究拓展至多目标跟踪中的类增量学习领域，这对于满足自主系统持续演进的需求至关重要。现有针对目标检测器的持续学习解决方案未能解决基于外观的跟踪器的数据关联阶段，导致对先前类别重识别特征的灾难性遗忘。我们提出COOLer——一种基于对比学习和持续学习的跟踪器，通过结合当前可用真实标签与历史跟踪器生成的伪标签进行训练，逐步学习跟踪新类别的同时保留过去知识。为进一步强化实例表征的解耦，我们引入新颖的对比式类增量实例表征学习技术。最后，我们为多目标跟踪中的持续学习提出实用评估协议，并在BDD和SHIFT数据集上进行实验。实验结果表明，COOLer在持续学习过程中有效解决了跟踪与检测双重维度的灾难性遗忘问题。代码已开源至\url{https://github.com/BoSmallEar/COOLer}。