Pedestrian re-recognition method based on depth-learning joint optimization

Abstract

The invention discloses a pedestrian re-identification method based on depth learning joint optimization, which comprises the following steps: 1, collecting and screening positive and negative pedestrian sample pairs with balanced quantity to construct a data set; 2, constructing a deep-learning Siamese neural network structure model, comprising a two-way front-end convolution neural network and amulti-level feature fusion module, input positive and negative pedestrian samples into that model, and extracting the Hyper features of two different pedestrian; 3, sending the Hyper features of twodifferent pedestrian into a classification network and a verification network, combining the classification network and the verification network, combining the classification los function and the verification loss function, and optimizing the parameters of the neural network structure model. In the method, the depth convolution neural network and HyperNet network are combined to extract multi-scale features to enhance the detection ability of pedestrian target, and the verification model and classification model are combined to optimize the network structure, and the excellent pedestrian re-recognition neural network structure model is obtained.

Description

technical field [0001] The invention belongs to the technical field of pedestrian re-identification in computer vision, and in particular relates to a pedestrian re-identification method based on deep learning joint optimization. Background technique [0002] Pedestrian re-identification is one of the important topics in the field of computer vision and pattern recognition. Pedestrian re-identification refers to retrieving a given pedestrian target in multiple cameras, and correlating and matching the retrieval results to quickly and accurately find the target pedestrian. Live footage and tracks under multiple cameras. Because of its important significance in the fields of intelligent video surveillance and multi-target tracking, it has attracted more and more attention from scientific researchers in related fields, governments and public security departments in recent years. [0003] Person re-identification mainly studies the use of visual features to match pedestrian obj...

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of loss and difference in the feature extraction of the existing pedestrian re-identification method, as well as the lack of feature discrimination, which leads to the performance decline in pedestrian re-identification, and proposes a pedestrian re-identification based on deep learning joint optimization In this method, the deep convolutional neural network and HyperNet network are used jointly to extract multi-scale features, enhance the detection ability of pedestrian targets, jointly verify the model and classification model, and jointly optimize the network structure with multiple loss functions to obtain a pedestrian re-identification neural network with superior performance. network structure model

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