this code is the official code for the paper :Adaptive Context Learning Network for Crowd Counting.which accepted by the IEEE SMC 2020. 论文地址 https://ieeexplore.ieee.org/document/9282944
Abstract—The task of crowd counting is to estimate the accurate number of people in photos taken from unconstrained surveillance scenes. It is in general a challenging problem due to the input scale variations and perspective distortions. Previous methods make efforts to enhance the representation ability by using multi-scale features of the scene pictures. However, most of these methods directly add or fuse the features, in which the influences of different feature sizes are equally considered. In this paper, we propose a novel architecture called adaptive context learning network (ACLNet) to incorporate context of features in multiple levels. In this architecture, the original image features are enhanced by a multi-level feature generating module, and then the multi-level features are up-sampled to the same size and re-weighted for fusing. The ACLNet incorporates the context information existed in sub-regions of various scales adaptively, thus it is able to enhance the representative ability of multi-level features. We perform several experiments on public ShanghaiTech (A and B), UCF CC 50 and NWPU-crowd datasets. Our proposed ACLNet achieves the state-of-the-art results compared with existing methods. Index Terms—crowd counting, density map, pyramid pooling, adaptive convolution
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Citation If you find this project is useful for your research, please cite:
@article{gao2019c, title={Adaptive Context Learning Network for Crowd Counting}, author={Zhao Liu 1 , Guanqi Zeng ∗ , Zunlei Feng 2 , Rong Zhang 1 , Mingli Song 2 , Jianping Shen}, journal={IEEE SMC2020}, year={2020} }