A neighbor-aware feature enhancement network for crowd counting

Abstract

Deep neural networks have achieved significant progress in the field of crowd counting in recent years. However, many networks still face challenges in effectively representing crowd features due to the insufficient exploitation of inter-channel and inter-pixel relationships. To overcome these limitations, we propose the Neighbor-Aware Feature Enhancement Network (NAFENet), a novel architecture designed to strengthen feature representation by adequately leveraging both channel and pixel dependencies. Specifically, we introduce two modules to model channel dependencies: the Across Channel Attention Module (ACAM) and the Channel Residual Module (CRM). ACAM computes a relevance map to quantify the influence of adjacent channels on the current channel and extracts valuable information to enrich the feature representation. On the other hand, CRM learns the residual maps between adjacent channels to capture their correlations and differences, enabling the network to gain a deeper understanding of the image content. In addition, we embed a Spatial Correlation Module (SCM) in NAFENet to model long-range dependencies between pixels across neighboring rows to analyze long continuous structures more effectively. Experimental results on six challenging datasets demonstrate that the proposed method achieves impressive performance compared to state-of-the-art models. Complexity analysis further reveals that our model is more efficient, requiring less time and fewer computational resources than other approaches.