Research On Multi-target Detection And Tracking Method Based On Deep Learning

In recent years,with the promotion of artificial intelligence technology and the widespread application of 5G functions,intelligent vehicles have achieved rapid development.The environment perception module responsible for understanding the surrounding environment of the car is a very critical part of the smart car system.The high-precision environmental perception module provides a safety guarantee for the smart car.The multi-target detection and tracking algorithm is a very important part of the environment perception module.At present,there is a problem that the computational complexity is high and it cannot run in real time on the embedded platform.Therefore,based on the design principle of lightweight network,this paper improves the multi-target detection and tracking algorithm,and finally realizes real-time operation on the embedded platform NVIDIA Jetson AGX Xavier.First,based on the SSD algorithm,two efficient convolutional neural network modules are designed,namely the context enhancement module and the feature enhancement module.And conducted training and accuracy verification on two public data sets,namely MS COCO and Pascal VOC,and conducted ablation experiments on each module on Pascal VOC.The results show that when the image input size is 320x320,on the MS COCO test set,the average detection accuracy of this algorithm is 8.5% higher than that of the original SSD algorithm.On the Pascal VOC test set,the average detection accuracy of this algorithm is increased by 3.7%compared to the original SSD algorithm,especially the accuracy of the small target category Bottle increased by 13.2%.Secondly,based on Didi’s D2-City data set,a training set and a test set for multi-target detection and tracking are made.Considering the problem that most multi-target tracking algorithms can only run on the server in real time,a lightweight multi-target tracking model is designed based on the Fair MOT algorithm.The lightweight backbone network Shuffle Netv2 is used for feature extraction,and then the context enhancement module and feature enhancement module are further optimized and inserted into the multi-target detection and tracking algorithm,and accuracy tests are carried out in three typical scenarios.Among them,the urban road scene tracking results show that when only the optimized context enhancement module is added to the model,the MOTA value increases from 51.9% to 61.1%.When the model adds the optimized context enhancement module and the optimized feature enhancement module at the same time,the MOTA value increases from 51.9% to 63.2%,and the ID Switch value is also reduced.And when the target temporarily disappears and then reappears,the algorithm has the ability to retrieve the trajectory label.Finally,the entire multi-target tracking algorithm is deployed on the embedded platform NVIDIA Jetson AGX Xavier.First,convert the convolutional neural network part from the Pytorch model to the onnx format,and then convert the onnx format to the ncnn format,and then convert the post-processing part of the algorithm and the tracking algorithm from the python program to the C++ program.The time test results on the embedded platform show that the algorithm in this paper takes 33.504 ms to process a picture,which meets the real-time requirements.In summary,this article optimizes the accuracy and running speed of the multi-target detection and tracking algorithm,and improves the tracking stability of the algorithm.After the algorithm is deployed on the embedded platform NVIDIA Jetson AGX Xavier,the processing time of each image meets the real-time requirements.