MG-GCT: A Motion-Guided Graph Convolutional Transformer for Traffic Gesture Recognition

计算机科学 人工智能 变压器 计算机视觉 手势 图形 语音识别 模式识别(心理学) 工程类 理论计算机科学 电气工程 电压
作者
Xiaofeng Guo,Qing Zhu,Yaonan Wang,Yang Mo
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:: 1-9
标识
DOI:10.1109/tits.2024.3394911
摘要

For autonomous driving systems, it is crucial to recognize the actions and gestures of traffic conductors and cyclists on the road to ensure safety. However, traffic gesture recognition is more challenging than action recognition in general scenarios due to the differences in action posture and sample composition between traffic gesture datasets and general action datasets. Therefore, general action recognition methods cannot identify traffic gestures well. To overcome these problems, we propose a novel motion-guided graph convolutional transformer (MG-GCT) for traffic gesture recognition. Firstly, we proposed a two-stream network to fully utilize joint data and motion data for action recognition. Secondly, we designed and implemented a motion-guided module between two streams, which leverages the powerful spatial representation ability of the motion data to guide the learning of the joint data stream in the spatial dimension. Thirdly, we implemented a temporal transformer network to process the temporal features of the skeleton. Finally, we conducted extensive experiments on two public datasets and one dataset presented by us to demonstrate the effectiveness of our network in traffic gesture recognition, which has a significant advantage over the state-of-the-art methods.
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