Adaptive Scale Selection Network for Crowd Counting.

Crowd counting is a computer vision task that focuses on accurately estimating the number of people present in a given scene. In the past few years, convolutional neural network-based deep learning techniques have achieved remarkable success in many computer vision tasks, including crowd counting. In the field of crowd counting, large-scale changes have always been a great challenge. To resolve this problem, previous work used multiple branches to obtain information at different scales and combined it. However, purely combining multi-branch features cannot effectively utilize multi-scale information. In this work, we modify the previous multi-branch architecture, which can reasonably select the appropriate scale information. Furthermore, we test our model on the ShanghaiTech dataset and demonstrate the competitive performance of our method.