Research On Traffic Police Gesture Recognition Based On Deep Learning Algorithm

As an important traffic signal,the gesture signal of traffic police plays an important role in ensuring the traffic safety of the congested section.The study on the flexible gesture recognition of traffic police is of great significance to the development of both assisted driving and autonomous driving technology.The current traffic police gesture recognition methods can be roughly divided into sensor-based and vision-based methods.Sensor-based police gesture recognition methods require the traffic police to wear a sensor which can collect gesture information.Although this kind of method can achieve relatively high accuracy,but this kind of method will affect the traffic police,increase the burden of traffic police,not suitable for practical application.The traditional vision-based gesture recognition method of traffic police requires Kinect device to collect depth images and manually design feature extractor.This method has low recognition accuracy,cannot meet the needs of assisted driving or automatic driving.In this paper,we use the deep learning method to study the traffic police gesture recognition.The research includes: making video data set of traffic police gesture,extracting gesture features of traffic police and recognizing gesture signals of traffic police.The main research achievements are as follows:A set of traffic police gesture signal data was collected and made,including 124×8 gesture video data.The feature extraction model of traffic police gesture based on deep convolutional neural network is established to extract the feature sequence of video data of traffic police gesture.Firstly,video frames images are extracted from the video data of traffic police gesture.Then,we train the traffic police gesture feature extraction model based on the Inception-v3 network design in the video image data set.The accuracy of this model is 0.709,indicating that the model has learned effective traffic police gesture features.Finally,the model is used to extract the gesture features of traffic police and obtain the feature sequence of traffic police gesture video.We designed two models for traffic police gesture recognition: TPGR-LSTM model based on LSTM and TPGR-Merged model fusing LSTM network and fully connect network.The TPGR-LSTM model refers to the design idea of GoogLeNet dan ResNet network,integrates the sequence features of LSTM network learning and the action features of fully connect network learning,in order to improve the model's fitting ability.After training,these two models have achieved high recognition accuracy in 8 kinds of traffic police gesture signals,among which the average recognition accuracy of TPGR-LSTM model is 95.6%,while the average recognition accuracy of TPGR-Merged model of traffic police gesture signals fused with LSTM network and fully connect network is further improve to 97.0%.