2021/05/24 by Mengxiao Tian, Hao Guo, Tian, Mengxiao +3
Computer Science · Social Sciences · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2105.11422
openalex publication_date 2021/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently the crowd counting has received more and more attention. Especially the technology of high-density environment has become an important research content, and the relevant methods for the existence of extremely dense crowd are not optimal. In this paper, we propose a multi-level attentive Convolutional Neural Network (MLAttnCNN) for crowd counting. We extract high-level contextual information with multiple different scales applied in pooling, and use multi-level attention modules to enrich the characteristics at different layers to achieve more efficient multi-scale feature fusion, which is able to be used to generate a more accurate density map with dilated convolutions and a 1× 1 convolution. The extensive experiments on three available public datasets show that our proposed network achieves outperformance to the state-of-the-art approaches.