A Lightweight Neural Network For Crowd Analysis Of Images With Congested Scenes
Xiangyu Ma, Shan Du, Yu Liu · 2019
For images with congested scenes, the task of crowd analysis, including crowd counting and crowd distribution prediction, becomes very difficult. To address these issues, various CNN-based approaches have been proposed. However, those methods usually have a large number of parameters and require huge computing resources. In this paper, we focus on low-complexity approaches and propose a lightweight endto-end network for crowd analysis. Our method utilizes an effective scale-aware module to extract multi-scale features and then regresses these features to density maps. The proposed network is consisted by three parts: multi-scale feature extraction, density map estimation and density map correction, and the network which only contains 0.86 M parameters (Lightweight). According to our experiments, our proposal obtain a better result than other existing methods on several testing sequences.