Crowd Counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the new-coming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks to handle multiple domains. 1) The models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies of intrinsic data distributions from various domains, which is called catastrophic forgetting. 2) The well-trained model in a specific domain achieves imperfect performance among other unseen domains because of the domain shift. 3) It leads to linearly-increased storage overhead either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigate a new task of crowd counting under the incremental domains training setting, namely, Lifelong Crowd Counting. It aims at alleviating the catastrophic forgetting and improving the generalization ability using a single model updated by the incremental domains. To be more specific, we propose a self-distillation learning framework as a benchmark~(Forget Less, Count Better, FLCB) for lifelong crowd counting, which helps the model sustainably leverage previous meaningful knowledge for better crowd counting to mitigate the forgetting when the new data arrive. Meanwhile, a new quantitative metric, normalized backward transfer~(nBwT), is developed to evaluate the forgetting degree of the model in the lifelong learning process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

翻译：众人计数在公共安全和大流行病控制方面有着重要的应用。 一个强大而实用的人群计数系统必须能够不断学习在现实世界情景中新到的域域数据,而不是只安装一个域。 现成的方法有一些缺点可以处理多个域。 1 由于不同域的内在数据分布因不同域的内在数据分布差异而从新领域获得培训图像后,这些模型将在旧域中实现有限的性能(甚至急剧下降),这被称为灾难性的遗忘。 2 在一个特定域中经过良好训练的模式由于域变换而在其他隐蔽域中实现不完善的性能。 3 它导致将所有用于培训的数据混合起来,或者仅仅在新领域培训数十个不同的模型。 要克服这些问题,我们调查在递增域培训环境(即Lifeloong Crowd Countinging)下进行人群计数的新任务。 它旨在减轻灾难性的遗忘,并使用由递增域更新的单一模型提高的普及能力。 更具体地说,我们建议一个自我消化的学习框架,作为基准: (FGLO, Beread, lavely, lead) learalalalalalalalalalalalalalalalalalal bal relight) leglevation a regilding lax to lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax laxxxxxx lax lax lax