Pedestrian image recognition method based on deep network model

Abstract

The invention provides a pedestrian image recognition method based on a deep network model. The pedestrian image recognition method comprises the steps of performing data preprocessing on a pedestrianimage; executing an adaptive sampling algorithm on the preprocessed data to obtain a batch with more difficult samples; extracting multilayer features through a backbone network model; enhancing thelow-level features by using a sub-module, performing downscaling, splicing the low-level features with the high-level features to obtain multi-level features, segmenting the multi-level features according to different granularities to form a multi-branch structure, extracting component features and global features of each branch, and splicing all the extracted features to obtain depth representation of the pedestrian image; training the constructed network model; extracting the depth representation of the query images through the trained network model, and returning the recognition result of each query image according to the cosine distance similarity between each query image and the queried set. Through the multi-level multi-granularity pedestrian re-recognition depth model, the optimal pedestrian re-recognition performance at the present stage is realized.

Description

technical field [0001] The invention relates to the fields of machine learning and computer vision, in particular to a pedestrian image recognition method based on a deep network model. Background technique [0002] With the development of modern society, public safety has gradually attracted people's attention. Shopping malls, apartments, schools, hospitals, office buildings, large plazas and other places with dense crowds and prone to public safety incidents have installed a large number of surveillance camera systems. The research on surveillance video is mainly reflected in the identification of visible objects, especially is pedestrian recognition. This is because pedestrians are generally the target of surveillance systems. More precisely, the task of the surveillance system is to search for a specific pedestrian in the surveillance video data, that is, the task of pedestrian re-identification. [0003] However, on the one hand, the data volume of surveillance video...

AI Technical Summary

Problems solved by technology

[0004] Among the existing pedestrian re-identification methods, the pedestrian re-identification method based on the component-based deep model has the most advanced performance. However, at the current stage, the component-based deep model often obtains component features by segmenting high-level features in the backbone network, and On the one hand, the high-level features of the deep model are highly coupled, and simply splitting the high-level features will lead to the loss of its semantic information, which will limit the performance of the model

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