Lightweight multimodal feature graph convolutional network for dangerous driving behavior detection

Author:

Wei Xing,Yao Shang,Zhao Chong,Hu Di,Luo Hui,Lu Yang

Funder

Intelligent Interconnected Systems Laboratory of Anhui Province

Natural Science Foundation of Yichang City

Anhui Provincial Key R &D Program

Intelligent Networking and New Energy Vehicle Special Project of Intelligent Manufacturing Institute of HFUT

Publisher

Springer Science and Business Media LLC

Subject

Information Systems

Reference41 articles.

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3. Caetano, C., Sena, J., Brémond, F., Dos Santos, J.A., Schwartz, W.R.: Skelemotion: a new representation of skeleton joint sequences based on motion information for 3d action recognition. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). pp. 1–8. IEEE (2019)

4. Cho, S., Maqbool, M., Liu, F., Foroosh, H.: Self-attention network for skeleton-based human action recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 635–644 (2020)

5. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inf. Process. Syst. 29 (2016)

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1. An Effective Multi-Scale Framework for Driver Behavior Recognition With Incomplete Skeletons;IEEE Transactions on Vehicular Technology;2024-01

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