Subway Driver Behavior Detection Method Based On Multi-features Fusion

The recognition of subway driver behavior is an important way for early warning of public safety. The current models of behavior recognition focus on action recognition of target objects in large-scene, which are difficult to apply for the subway driver behavior recognition directly because of space-time constraints. RepC3D model is proposed for recognizing subway driver behaviors in the paper. The model fuse the features of C3D model and RepVGG model. Firstly we preprocess the dataset by cutting the subway driver operation video into short videos, then the preprocessed dataset is adopted as the input of RepC3D model and is downsampled with the multiscale convolution layers of the main network VGG, which is used to extract the effective features of the driver's action behavior. Next, as the feature tranning network,RepC3D model identify and classfy the behaviors of the subway driver from the videos. The experimental result shows that the RepC3D model is btteetter than the C3D model and RepVGG model in terms of recognition accuracy, false detection rate, and missed detection rate, the recognition efficiency is also improved. The dataset is available at https://github.com/wtazyy/Datasets.git.