HARNet: Hierarchical adaptive regression with location recovery for crowd counting

Researchers have started to utilize the structures like Feature Pyramid Network (FPN) for crowd counting to extract multi-scale features, which can solve the scale variation problem in traditional density map regression-based methods. However, many downsampling operations in FPN inevitably induce loss of location information, resulting in pixel mismatch of the predicted density map. In this paper, we propose the Location Recovery Module (LRM) to recover the loss of location information. Unlike the basic feature fusion module in FPN, the proposed LRM builds an offset map to modify the position of the downsampled features under the guidance of low-level features. Meanwhile, we design a novel regression strategy to fully utilize the multi-scale information without upsampling operations in the fusion process. Considering that the highest-level feature cannot represent all density-level features well, different-scale features focus more on their related regions in our regression strategy. The proposed HARNet has been tested on four datasets and achieved state-of-the-art on two datasets. The performance metric such as arrives at 52.4, 5.9 and 181.7 on ShanghaiTech A/B and UCF_CC_50 respectively.