PAND: Precise Action Recognition on Naturalistic Driving
Author:
Affiliation:
1. Beijing University of Posts and Telecommunications
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9856930/9856648/09857114.pdf?arnumber=9857114
Reference34 articles.
1. Thumos challenge: Action recognition with a large number of classes;yugang jiang,2014
2. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
3. R-c3d: Region convolutional 3d network for temporal activity detection;xu;Proceedings of the IEEE International Conference on Computer Vision,2017
4. VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions
5. Actionformer: Localizing moments of actions with transformers;zhang,2022
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1. M2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06
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