  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 incoming domain data in real-world scenarios
instead of fitting one domain only. Off-the-shelf methods have some drawbacks
when handling 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; and (3) it leads to linearly increasing 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 investigated a new crowd counting task in the incremental
domains training setting called Lifelong Crowd Counting. Its goal is to
alleviate the catastrophic forgetting and improve the generalization ability
using a single model updated by the incremental domains. Specifically, we
propose a self-distillation learning framework as a benchmark (Forget Less,
Count Better, or 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. In addition, 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.
