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作者:

He, Jian (He, Jian.) | Jiang, Shengsheng (Jiang, Shengsheng.) | Wei, Xin (Wei, Xin.) | Zhang, Cheng (Zhang, Cheng.) | Dong, Ruihai (Dong, Ruihai.)

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EI Scopus

摘要:

According to the requirement of recognizing traffic police gestures for driver assistance systems and intelligent vehicles, a universal model for dynamic traffic police gesture recognition is firstly introduced, of which can accurately present the spatial context (such as the relative lengths of skeletons, the angles between each skeleton w. r. t. gravity, and part features) of the traffic police gestures. Secondly, an architecture which can respectively extract spatial context and temporal features of dynamic traffic police gesture is proposed. Meanwhile, deep neural network and LSTM are introduced to build a high-resolution traffic police gestures recognizer (namely HRTPGR). At last, the open Police Gesture Dataset is used to train and test TPGR, and the experimental results show that the TPGR achieves a state-of-the-art accuracy with 98.7% for dynamic traffic police gestures recognition, and has strong anti-interference ability to light, background and gesture shape changes. © 2023 IEEE.

关键词:

Statistical tests Musculoskeletal system Automobile drivers Long short-term memory Deep neural networks Law enforcement Gesture recognition

作者机构:

  • [ 1 ] [He, Jian]Beijing Engineering Research Center for IOT Software and Systems, Beijing University of Technology, Beijing, China
  • [ 2 ] [Jiang, Shengsheng]Beijing University of Technology, Faculty of Information Technology, Beijing, China
  • [ 3 ] [Wei, Xin]Beijing University of Technology, Faculty of Information Technology, Beijing, China
  • [ 4 ] [Zhang, Cheng]Beijing University of Technology, Faculty of Information Technology, Beijing, China
  • [ 5 ] [Dong, Ruihai]School of Computer Science University College Dublin, Dublin, Ireland

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年份: 2023

页码: 114-119

语种: 英文

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