Scale Selection Network with Attention Mechanism for Crowd Counting
Ting-Hsu Lai, Tsung-Jung Liu, Kuan-Hsien Liu · 2023
Crowd counting is an important cornerstone in tasks related to crowd analysis. In recent years, there has been a significant rise in the development of deep learning methods, particularly those based on Convolutional Neural Networks (CNN), in the field of image processing and achieved amazing results. However, there are some challenges waiting to be solved in the current crowd counting task: large-scale variations in crowd sizes and interference from the background. Both of them will lead to poor prediction results. Therefore, we propose a scale selection module to deal with the scale variation problem in images. And for background interference, we proposed an attention module to reduce the interference of background information. Moreover, we evaluated our model on four commonly used datasets and compared the performance with other state-of-the-art methods to demonstrate the competitiveness of our approach. The source code and pretrained models are available at https://git;hub.com/TimLai0307/SSN.