An unsupervised pedestrian re-recognition method based on fuzzy depth clustering

Abstract

The embodiment of the invention discloses an unsupervised pedestrian re-identification method based on fuzzy depth clustering. The method comprises the following steps: extracting pedestrian image features by using pedestrian image feature extraction network model; extracting pedestrian image features by using the pedestrian image feature extraction network model; extracting pedestrian image features by using fuzzy depth clustering. Construct fuzzy depth clustering network and initialize it; The fuzzy depth clustering network is used to learn the new feature space and clustering center, and the fuzzy labels are assigned to the unlabeled pedestrian images. Using reliability samples to train the pedestrian image feature extraction network model; Alternate training until the reliability sample is saturated; Using the trained pedestrian image feature extraction network model to extract the test pedestrian image features, the unsupervised pedestrian re-recognition results are obtained by calculating the feature distance. The invention utilizes fuzzy depth clustering network to learn new feature space, which is favorable for clustering of complex pedestrian images and distribution of fuzzy labels, and utilizes reliability samples with fuzzy labels to train the feature extraction network, thereby reducing the risk of over-fitting and improving the correct rate of unsupervised pedestrian recognition and matching.

Description

technical field [0001] The invention belongs to the technical fields of computer vision, pattern recognition and artificial intelligence, and in particular relates to an unsupervised pedestrian re-identification method based on fuzzy depth clustering. Background technique [0002] The goal of pedestrian re-identification (Re-identification) is to determine whether pedestrians appearing under different cameras belong to the same pedestrian, which can be regarded as a sub-problem of image retrieval. With the continuous development of society, more and more cameras are installed in public places, such as shopping malls, communities, campuses, airports and so on. Through the monitoring of pedestrians in public places, it is convenient to collect the crime process of criminals and provide more clues for the police to solve the case. At the same time, video surveillance has reasonably restrained pedestrians, ensuring people's social and public safety. Due to the different shooti...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the high degree of nonlinearity and inseparability of the original feature space and the use of k-means clustering to obtain a single label have a

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