Lightweight multi-scale network with attention for accurate and efficient crowd counting

Crowd counting is a significant task in computer vision, which aims to estimate the total number of people appeared in images or videos. However, it is still very challenging due to the huge scale variation and uneven density distributions in dense scenes. Moreover, although many works have been presented to tackle these issues, these methods always have a large number of parameters and high computation complexity, which leads to a limitation to the wide applications in edge devices. In this work, we propose a lightweight method for accurate and efficient crowd counting, called lightweight multi-scale network with attention. It is mainly composed of four parts: lightweight extractor, multi-scale features extraction module (MFEM), attention-based fusion module (ABFM), and efficient density map regressor. We design the MFEM and ABFM delicately to obtain rich scale representations, which is significantly beneficial for improving the counting accuracy. Moreover, the normalized union loss function is proposed to balance contribution of samples with diverse density distributions. Extensive experiments carried out on six mainstream crowd datasets demonstrate that our proposed method achieves superior performance to the other state-of-the-art methods with a small model size and low computational cost.