Pedestrian re-identification method, system, electronic device and storage medium

Abstract

A pedestrian re-identification method, system, electronic device and computer-readable storage medium are disclosed. The method includes: acquiring a video set and determining a target pedestrian image; Extracting target features of the target pedestrian image, and inputting the target features into the NSN to extract an image to be recognized of each frame of the image in the video set; Generating an optical flow map of an image to be recognized of adjacent frames by using an MN network; All the images to be identified and all the optical flow images are input into the LSTM network to obtaina fusion picture fusing multi-frame image information. The global feature map and partial attention map of the fusion image are extracted from the trained human feature extraction network, and each part of the attention map and the global feature map are fused into a fused partial attention feature map respectively. The fusion feature vectors of each part of the attention fusion feature map are formed by the global average pool, and all the fusion feature vectors are connected to the global feature vectors, which improves the recognition accuracy of pedestrian re-recognition.

Description

technical field [0001] The present application relates to the field of computer technology, and more specifically, to a pedestrian re-identification method and system, an electronic device, and a computer-readable storage medium. Background technique [0002] Pedestrian re-identification is an important technology in public security. It can play a big role in finding lost people and searching for criminals. Due to the advancement of convolutional neural network technology, pedestrian re-identification technology has begun to advance. The current pedestrian re-identification uses convolutional recognition networks based on the local features of single-frame images. It is necessary to align each body part of the image to be recognized with the image of the target pedestrian. , otherwise there may be errors similar to the comparison between the head and the upper body, and there will be noise interference, resulting in inaccurate recognition results. [0003] Therefore, how to...

AI Technical Summary

Problems solved by technology

Due to the advancement of convolutional neural network technology, pedestrian re-identification technology has begun to advance. The current pedestrian re-identification uses convolutional recognition networks based on the local features of single-frame images. It is necessary to align each body part of the image to be recognized with the image of the target pedestrian. , otherwise there may be an error similar to the comparison between the head and the upper body, and there will be noise interference, resulting in inaccurate recognition results

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