Pedestrian re-identification method based on multi-region feature extraction and fusion

Abstract

The invention relates to a pedestrian re-identification method based on multi-region feature extraction and fusion, which uses residual network to extract global features, and adds a pedestrian identity classification module for global feature extraction and optimization in the training stage; constructs for local features The extracted multi-region feature extraction sub-network, and weighted fusion of each local feature; set the loss function including the loss of the classification module and the loss of the feature fusion module; train the network to obtain the feature vector of the model extraction query set and test set; In the measurement phase, the feature distance is re-measured using the cross-nearest neighbor method. The invention has a reasonable design, effectively combines global features and local features, optimizes the distance measurement method, obtains good pedestrian re-identification results, and greatly improves the overall matching accuracy of the system.

Description

technical field [0001] The invention belongs to the technical field of computer vision pedestrian re-identification, in particular to a pedestrian re-identification method based on multi-region feature extraction and fusion. Background technique [0002] With the continuous improvement of video capture technology and the rapid development of large-scale data storage technology, it is possible to apply a large number of surveillance camera systems in public places. The identification and processing of pedestrians in massive surveillance videos presents a trend of rapid development. Relying only on human eyes to identify pedestrians in surveillance images is obviously very inefficient. The task of pedestrian re-identification technology is to rely on computer vision technology to solve non-overlapping surveillance fields of view. The problem of identity matching of pedestrians. [0003] There are two main categories of traditional solutions: one is feature extraction, which i...

AI Technical Summary

Problems solved by technology

[0004] However, most of the existing convolutional neural networks extract the features of the whole picture, and do not make full use of the local features of the picture. These local features are more robust to the pose changes of pedestrians under different viewing angles, and are more effective for distinguishing different pedestrians. , therefore, the overall matching accuracy of existing person re-identification methods is not high

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