A fast occluded passenger detector based on MetroNet and Tiny MetroNet

Abstract

Metro passenger detection is always a significant task and a bottleneck in metro video surveillance system. Much recent research has demonstrated that Convolutional Neural Network (CNN) is more powerful than other machine learning algorithms in numerous computer vision tasks. Motivated by the research, this paper proposes MetroNet and Tiny MetroNet for detecting occluded metro passengers in metro embedded system with limited hardware resources. MetroNet consists of smaller CNN-SqueezeNet, Region Proposal Network (RPN) and Detection Head subnet. Besides, the repulsion loss is adopted to effectively prevent detection results from worsening caused by severe passengers’ occlusion during training phase. On the other hand, considering that some platforms have more limited hardware resources, a simple version of the MetroNet named Tiny MetroNet is designed and a novel, tiny passenger feature network is proposed as backbone. Based on three datasets, two MetroNets are tested and compared to existing state-of-the-art detection networks on CPU and GPU mode. The experiment results demonstrate that MetroNet has real-time performance and better detection accuracy. Tiny MetroNet achieves fast detection speed and smaller model size with acceptable performance degradation. Even for the ARM embedded system, their performance is competitive and can meet the application requirements of high-speed metros.