Crowd Counting using Deep Recurrent Spatial-Aware Network

Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera’s perspective that causes huge appearance variations in people’s scales and rotations. Conventional methods address such challenges by resorting to fixed multi-scale architectures that are often unable to cover the largely varied scales while ignoring the rotation variations. In this paper, we propose a unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise refinement process. Specifically, our framework incorporates a Recurrent Spatial-Aware Refinement (RSAR) module iteratively conducting two components: i) a Spatial Transformer Network that dynamically locates an attentional region from the crowd density map and transforms it to the suitable scale and rotation for optimal crowd estimation; ii) a Local Refinement Network that refines the density map of the attended region with residual learning. Extensive experiments on four challenging benchmarks show the effectiveness of our approach. Specifically, comparing with the existing best-performing methods, we achieve an improvement of 12\% on the largest dataset WorldExpo’10 and 22. 8\% on the most challenging dataset UCF\_CC\_50

Authors

• Lingbo Liu Pengcheng Laboratory
• Hongjun Wang Southern University of Science and Technology
• Guanbin Li Sun Yat-sen University GuangDong Province Key Laboratory of Information Security Technology
• Wanli Ouyang Shanghai Artificial Intelligence Laboratory
• Liang Lin Sun Yat-Sen University

Keywords

• Computer Vision: Computer Vision
• Computer Vision: Video: Events, Activities and Surveillance
• Machine Learning: Deep Learning