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}.

这篇研究论文研究了连续学习在多目标跟踪中的应用，提出了一种基于对比学习和连续学习的跟踪器COOLer，可以在学习新类别的同时保留过去的知识，并通过实验证明它能够有效解决跟踪和检测的灾难性遗忘问题。

COOLer：面向基于外观的多目标跟踪的课程增量学习