Multiscale Feature Adaptive Integration for Crowd Counting in Highly Congested Scenes

Abstract

Due to extreme scale variations in highly congested scenes, the accuracy of CNN-based crowd counting approaches still has considerable room for further improvements. In this paper, we propose a new multi-scale feature adaptive integrated network (MSFAINet) for crowd counting that adopts the multi-scale feature, hybrid attention, and dilated convolution. First, the proposed MSFAINet extracts feature maps from different levels by the improved VGG16 and focuses on more important information that represents features at different scales. Second, it adopts a hybrid attention mechanism to enhance the receptive field of an image while reducing the loss of feature information caused by channel competition and then passes these features into the dilated convolution combined with the traditional convolution. Finally, it generates the density estimation map by accelerating the convergence of the network. The proposed MSFAINet is used to conduct extensive studies to demonstrate the effectiveness of the approach on several mainstream datasets. From the experimental results, MSFAINet can extract and retain more detailed information and greatly reduce the influence of scale variations in crowd counting.