Multi-head multi-scale pixel localization network for crowd counting with highly dense and small-scale samples
Haoyuan Ma, Li Zhang · 2024
Crowd counting plays an important role in computer vision. However, existing methods mainly depend on largescale samples and then make a success in low and medium dense scenes. In addition, current methods suffer from noise introduced by generating density maps and lose the crowd location information. To remedy these issues, we propose a multihead multi-scale pixel localization network (M2PLNet), which can effectively localize the crowds and perform well in the counting task with highly dense and small-scale samples. To address challenges posed by occlusion, noise, and other factors, we design the centralized asymptotic feature pyramid (CAFP) module. This module effectively fuses information from multiple scales, enhancing the capability to identify and count individuals across various angles and distances. Our approach concludes with results obtained through four sets of classification and regression heads. As confirmed by a large number of experiments, M2PLNet achieves the state-of-the-art results on two datasets (ShangHaiTech PartA and UCF CC 50) that are highly dense and with small-scale samples, which fully demonstrates the effectiveness and superiority of M2PLNet. Code is available at: https://github.com/Jason-Mar1/M2PLNet/tree/master.