Chinese Traffic Police Gesture Recognition in Complex Scene

2011 ◽  
Author(s):  
Fan Guo ◽  
Zixing Cai ◽  
Jin Tang
Keyword(s):  
Gesture Recognition ◽  
Complex Scene ◽  
Traffic Police ◽  
Chinese Traffic Police

scholarly journals Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene

2021 ◽  
Vol 11 (24) ◽  
pp. 11951
Author(s):  
Kang Liu ◽  
Ying Zheng ◽  
Junyi Yang ◽  
Hong Bao ◽  
Haoming Zeng
Keyword(s):  
Gesture Recognition ◽  
Physical Structure ◽  
Attention Mechanism ◽  
Graph Structure ◽  
Automated Driving ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Traffic Lights ◽  
Traffic Police ◽  
Chinese Traffic Police

For an automated driving system to be robust, it needs to recognize not only fixed signals such as traffic signs and traffic lights, but also gestures used by traffic police. With the aim to achieve this requirement, this paper proposes a new gesture recognition technology based on a graph convolutional network (GCN) according to an analysis of the characteristics of gestures used by Chinese traffic police. To begin, we used a spatial–temporal graph convolutional network (ST-GCN) as a base network while introducing the attention mechanism, which enhanced the effective features of gestures used by traffic police and balanced the information distribution of skeleton joints in the spatial dimension. Next, to solve the problem of the former graph structure only representing the physical structure of the human body, which cannot capture the potential effective features, this paper proposes an adaptive graph structure (AGS) model to explore the hidden feature between traffic police gesture nodes and a temporal attention mechanism (TAS) to extract features in the temporal dimension. In this paper, we established a traffic police gesture dataset, which contained 20,480 videos in total, and an ablation study was carried out to verify the effectiveness of the method we proposed. The experiment results show that the proposed method improves the accuracy of traffic police gesture recognition to a certain degree; the top-1 is 87.72%, and the top-3 is 95.26%. In addition, to validate the method’s generalization ability, we also carried out an experiment on the Kinetics–Skeleton dataset in this paper; the results show that the proposed method is better than some of the existing action-recognition algorithms.

Max-covering scheme for gesture recognition of Chinese traffic police

2014 ◽  
Vol 18 (2) ◽  
pp. 403-418 ◽  
Author(s):  
Zixing Cai ◽  
Fan Guo
Keyword(s):  
Gesture Recognition ◽  
Traffic Police ◽  
Chinese Traffic Police

scholarly journals Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Peng Liu ◽  
Xiangxiang Li ◽  
Haiting Cui ◽  
Shanshan Li ◽  
Yafei Yuan
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Learning Algorithm ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Single Shot ◽  
Complex Scene ◽  
Hand Gestures ◽  
Benchmark Database

Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.

Gesture recognition of traffic police based on static and dynamic descriptor fusion

2016 ◽  
Vol 76 (6) ◽  
pp. 8915-8936 ◽  
Author(s):  
Fan Guo ◽  
Jin Tang ◽  
Xile Wang
Keyword(s):  
Gesture Recognition ◽  
Traffic Police ◽  
Descriptor Fusion

scholarly journals Traffic Police Gesture Recognition Based on Gesture Skeleton Extractor and Multichannel Dilated Graph Convolution Network

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 551
Author(s):  
Xin Xiong ◽  
Haoyuan Wu ◽  
Weidong Min ◽  
Jianqiang Xu ◽  
Qiyan Fu ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Background Noise ◽  
State Of The Art ◽  
The Other ◽  
Recognition Method ◽  
Accuracy Rate ◽  
Traffic Police ◽  
Automatic Driving ◽  
Rgb Images ◽  
Public Dataset

Traffic police gesture recognition is important in automatic driving. Most existing traffic police gesture recognition methods extract pixel-level features from RGB images which are uninterpretable because of a lack of gesture skeleton features and may result in inaccurate recognition due to background noise. Existing deep learning methods are not suitable for handling gesture skeleton features because they ignore the inevitable connection between skeleton joint coordinate information and gestures. To alleviate the aforementioned issues, a traffic police gesture recognition method based on a gesture skeleton extractor (GSE) and a multichannel dilated graph convolution network (MD-GCN) is proposed. To extract discriminative and interpretable gesture skeleton coordinate information, a GSE is proposed to extract skeleton coordinate information and remove redundant skeleton joints and bones. In the gesture discrimination stage, GSE-based features are introduced into the proposed MD-GCN. The MD-GCN constructs a graph convolution with a multichannel dilated to enlarge the receptive field, which extracts body topological and spatiotemporal action features from skeleton coordinates. Comparison experiments with state-of-the-art methods were conducted on a public dataset. The results show that the proposed method achieves an accuracy rate of 98.95%, which is the best and at least 6% higher than that of the other methods.

Traffic Police Gesture Recognition by Pose Graph Convolutional Networks

2020 ◽  
Author(s):  
Zhijie Fang ◽  
Wuqiang Zhang ◽  
Zijie Guo ◽  
Rong Zhi ◽  
Baofeng Wang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Traffic Police ◽  
Convolutional Networks
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