Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis, achieving tremendous success recently with the development of deep learning. However, there have been still many challenges including crowd multi-scale variations and high network complexity, etc. To tackle these issues, a lightweight Res-connection multi-branch network (LRMBNet) for highly accurate crowd counting and localization is proposed. Specifically, using improved ShuffleNet V2 as the backbone, a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters. A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields, where the information transmission and fusion of diverse scale features is enhanced via residual concatenation. In addition, a compound loss function is introduced for training the method to improve global context information correlation. The proposed method is evaluated on the SHHA, SHHB, UCF-QNRF and UCF_CC_50 public datasets. The accuracy is better than those of many advanced approaches, while the number of parameters is smaller. The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting, indicating a lightweight and high-precision method for crowd counting.