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 multiple object tracking (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 BDD100K 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 https://github.com/BoSmallEar/COOLer.

翻译：持续学习允许模型在不依赖先前任务训练数据的情况下，按顺序学习多个任务并保留旧知识。本文将持续学习研究拓展至多目标跟踪（MOT）的类增量学习领域，这对于满足自主系统不断演化的需求具有重要意义。现有针对目标检测器的持续学习方案未能解决基于外观的跟踪器的数据关联阶段问题，导致对先前类别重识别特征的灾难性遗忘。我们提出COOLer——一种基于对比与持续学习的跟踪器，通过结合当前可用真实标签与先前跟踪器生成的伪标签进行联合训练，逐步学习新类别的跟踪能力并保持已有知识。为强化实例表示的解耦性，我们进一步提出一种新颖的对比类增量实例表示学习技术。最后，我们为MOT持续学习设计了实用评估协议，并在BDD100K和SHIFT数据集上开展实验。结果表明，COOLer能够在持续学习过程中有效缓解跟踪与检测的双重灾难性遗忘问题。代码已开源：https://github.com/BoSmallEar/COOLer。