Cross-Camera Based Pedestrian Tracking Algorithm For Siamese Network With Attention Mechanism

In computer vision,the classic problem discussed by pedestrian target tracking can be applied to many fields,such as smart transportation,smart security and so on.At present,some methods of target tracking under a single camera have achieved a good level,but the problem of pedestrian tracking under a cross-camera is still a difficult problem at this stage and a scientific research issue that needs to be solved urgently.Therefore,this paper mainly studies the problem of cross-camera pedestrian matching and single-camera pedestrian tracking under the deep learning framework.The specific research contents are as follows:First of all,this paper makes an in-depth discussion on the current research status and basic concepts of the existing target and algorithm and pedestrian re-identification.And some basic algorithms of neural network are studied and analyzed.After that,this thesis researched the pedestrian tracking algorithm based on multilevel feature parallel mutual convolution.In this paper,based on the Siamese tracker,the network model is modified,and the attention module and deformable convolutional layer are added.The newly proposed network structure will extract features from the image.The features extracted by the model have more advantages.The convoluted image features are convolved with the picture to be matched to obtain the tracking result.Experiments show that the single-camera pedestrian tracking algorithm proposed in this paper can track pedestrian targets well in the video,and has improved the tracking accuracy.Besides,this paper discusses and studies the cross-camera pedestrian target matching problem based on the multi-level feature cascade pedestrian re-recognition algorithm.The process of pedestrian matching is generally to extract image features through the model,and then calculate the distance between different image features.Most algorithms only extract the features of the last layer during the model training phase,and do not make good use of the features of the previous layer,so that the accuracy of pedestrian matching is limited to a certain extent.In response to these problems,this paper proposes a new pedestrian matching Technology,extracting features through the basic network to obtain multiple levels of features,through attention weighting,fusion of multi-level features.Each level feature extracts two different scale features,retains each level feature and the fused feature,and calculates multiple loss functions.The pedestrian matching algorithm finally obtained in this paper has higher pedestrian matching accuracy.Finally,the thesis validates the single-camera pedestrian target tracking method and the cross-camera pedestrian target matching method through experiments,and combines the two methods to achieve cross-camera pedestrian target tracking.