The Design And Implementation Of Traffic Video Analysis System Based On Deep Learning

In recent years,with more and more cars entering ordinary families,private cars occupy an increasingly important position in people's daily life.At the same time,with the rapid increase of the number of cars,more and more illegal activities related to the car,so the structural analysis of traffic video is an urgent need in the field of security.With the rapid development of artificial intelligence technology,more and more attention has been paid to the field of intelligent security.As an important part of the security field,traffic video analysis has also attracted much attention.In the early years,due to the immature technology,video analysis of complex scenes has been difficult to achieve.However,with the emergence of deep learning,the video structuring of this large-scale complex scene becomes possible.At present,deep learning has been widely used in various fields of research and application,such as image recognition,speech recognition,and natural language process.Traffic video structure mainly relies on the intelligent analysis of monitoring video,and extracts basic information of the vehicle from the video,including the vehicle color,brand,license plate and other information.The extracted information will be saved to the database for later analysis and effective information mining.In the process of traffic video,vehicle and license plate detection,license plate recognition,vehicle brand identification and target tracking are in volved.In this paper,a series of methods are proposed to solve the problem of image recognition in the process of structuring traffic video with the latest techniques in target detection,OCR,image classification and target tracking.At the same time,i n the process of developing the system,a framework for training and reasoning of deep learning models has been developed.In view of vehicle detection and license plate detection in traffic video analysis,a fast target detection algorithm based on deep convolution neural network is used in this paper.The algorithm combines the existing one stage detection algorithm and uses the IOU loss instead of the traditional L1 loss.In view of the recognition of th e character and color of the license plate,this paper combines the deep convolution network,the CTC loss function and the cross entropy,and completes the recognition of the character and color simultaneously in a single frame.In view of the recognition of vehicle color and property,this paper uses a deep convolution neural network,focal loss and cross entropy to complete the classification of vehicle brand and color in a single frame.For multi target vehicle tracking,this paper combines detection re sults and Kalman filter,and tracks vehicle trajectory according to various attributes,and obtains real-time tracking effect.At the same time,the deep learning framework developed in this paper uses GPU in the training process,and GPU and AI chips are used in the inference process.After the test of the actual environment,the algorithm proposed in this paper has achieved a precision of more than 0.9 map in the search of the vehicle and the license plate.The recognition algorithm of the character and color of the license plate has obtained the precision of the character 98.7% and the color 99.5%,and the recognition of the brand and color of the vehicle also obtained the accuracy of 90% and 96%.