Abstract
Real-time detection and identification of dangerous driving behaviors is an effective measure to reduce traffic accidents. Due to the high network delay, limited communication bandwidth, and weak computing power, lightweight detection models that can run on edge devices have been widely investigated and attracted considerable attention. In recent years, the Graph Convolutional Network (GCN), which models the human skeleton as a spatiotemporal graph, has achieved remarkable performance, due to its powerful capability of modeling non-Euclidean structure data. However, there are disadvantages such as the single way of extracting information, high model complexity, and inability to integrate environmental information. Therefore, we propose a lightweight dangerous driving behavior detection model based on GCN. First, two local information extraction modules are designed to extract skeleton information features. Meanwhile, we propose a multi-information fusion behavior recognition model of “people \( {+} \) objects” by capturing the motion information of related object. Finally, the method based on Singular Value Decomposition (SVD) rank reduction is used to compress the model to improve the speed of recognizing an action sample under sufficient detection accuracy. The proposed model respectively achieves 96% and 86.3% accuracy on the x-view benchmark of NTU-RGBD dataset and the homemade Locomotive Driver Dataset, which attains the state-of-the-art performance.
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Acknowledgements
This work was supported by Joint Fund of Natural Science Foundation of Anhui Province in 2020 (2008085UD08), Anhui Provincial Key R &D Program (202004a05020004), Open fund of Intelligent Interconnected Systems Laboratory of Anhui Province (PA2021AKSK0107), Intelligent Networking and New Energy Vehicle Special Project of Intelligent Manufacturing Institute of HFUT (IMIWL2019003, IMIDC2019002).
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Wei, X., Yao, S., Zhao, C., Hu, D., Luo, H., Lu, Y. (2022). Graph Convolutional Networks (GCN)-Based Lightweight Detection Model for Dangerous Driving Behavior. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_3
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