Visual Recognition of Chinese Traffic Police Gestures Based on Spatial Context and Temporal Features

ZHANG Cheng; HE Jian; WANG Wei-dong

doi:10.3969/j.issn.0372-2112.2020.05.018

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Visual Recognition of Chinese Traffic Police Gestures Based on Spatial Context and Temporal Features

更新时间：2025-07-16

- Visual Recognition of Chinese Traffic Police Gestures Based on Spatial Context and Temporal Features
- Acta Electronica Sinica Vol. 48, Issue 5, Pages: 966-974(2020)
- 作者机构：
  
  北京工业大学信息学部,北京,100124
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61602016);Science and Technology Project of Beijing Municipality (No.D171100004017003)
- DOI：10.3969/j.issn.0372-2112.2020.05.018
  CLC： TP391.4
- Published Online：25 May 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
ZHANG Cheng, HE Jian, WANG Wei-dong. Visual Recognition of Chinese Traffic Police Gestures Based on Spatial Context and Temporal Features[J]. Acta Electronica Sinica, 2020, 48(5): 966-974.
DOI：

ZHANG Cheng, HE Jian, WANG Wei-dong. Visual Recognition of Chinese Traffic Police Gestures Based on Spatial Context and Temporal Features[J]. Acta Electronica Sinica, 2020, 48(5): 966-974. DOI： 10.3969/j.issn.0372-2112.2020.05.018.

摘要

针对无人驾驶汽车快速准确识别交警指挥手势的需求，本文在分析交警指挥手势的关节铰接特征基础上，建立基于关节点和骨架的交警指挥手势模型；其次，引入卷积姿势机（Convolutional Pose Machine，CPM）提取交警指挥手势的关键节点，进而提取交警指挥手势中骨架的相对长度及其与重力加速度的夹角作为空间上下文特征，并引入长短时记忆网络（Long Short Term Memory，LSTM）提取交警指挥手势的时序特征；最后，设计了融合空间上下文和时序特征的交警指挥手势识别机（Chinese Traffic Police Gesture Recognizer，CTPGR），创建了包含8种交警指挥手势、时长约2小时的交警指挥手势视频库对CTPGR进行训练验证，并通过实验将CTPGR与已有交警手势识别算法进行了对比分析.实验证明CTPGR可以快速准确地识别交警指挥手势，系统对复杂背景和动态交警指挥手势具有较强的适应能力.

Abstract

According to the need for driver assistance systems and intelligent vehicles to quickly and accurately identify traffic police command gestures

the articulated features of traffic police gesture is firstly analyzed

and a model based on the key points and skeletons of the police gesture is established. Secondly

the convolutional posture machine (CPM) is introduced to extract the key points of the traffic police gesture. Then the relative lengths of the gesture skeletons and the angles between each skeleton w.r.t. gravity are extracted as the spatial context features of the traffic police gesture. Meanwhile

long-term memory (LSTM) is introduced to extract the temporal features of traffic police gestures. Finally

the Chinese traffic police gesture recognizer (CTPGR) based on CPM and LSTM is designed

and a two-hour traffic police gesture video is recorded to train and verify the CTPGR. Experimental results show that the CTPGR is capable of recognizing traffic police gestures with high accuracy

and is fast enough for online gesture prediction.

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