Cross-scene pedestrian searching method based on depth learning

Abstract

The present invention discloses a cross-scene pedestrian searching method based on depth learning. The method comprises a step of carrying out preprocessing on each image in a sample library, a step of constructing and training a convolutional neural network, a step of extracting an upper half body local feature vector set and a lower half body local feature vector set from two groups of preprocessed image sets, and then the two local feature vector sets are fused to obtain a global feature vector, a step of carrying out preprocessing on an image to be searched, extracting an upper half body local feature vector and a lower half body local feature vector and fusing the two vectors to obtain a global feature vector, a step of orderly comparing the global feature vector corresponding to the image to be searched and the global feature vectors corresponding to the sample library images through a cosine similarity, outputting a group of similarity values, and sorting the similarity values according to a sorting algorithm. The method has the advantages that with the pedestrian images obtained in a monitoring video as the sample library, the design of features is not needed, the robustness is high, and the accuracy rate of actual searching is high.

Description

technical field [0001] The invention relates to the technical field of information technology, in particular to a deep learning-based cross-scene pedestrian search method. Background technique [0002] With the launch of the safe city strategy, more and more network surveillance cameras are installed in large squares, shopping malls, companies, hospitals, parks, schools, subway stations and other places with dense crowds and prone to public safety incidents. When an incident occurs, it is necessary to find the suspicious target person from the surveillance video images of multiple cameras, and these surveillance cameras are installed in various places with a large span, so that the staff can find the suspicious target pedestrian from multiple surveillance video images pose great challenges. There are many traditional search and comparison methods, such as based on color, texture and outline, etc., all of which use public libraries as sample libraries, and feature design i...

AI Technical Summary

Problems solved by technology

There are many traditional search and comparison methods, such as based on color, texture and outline, etc., all of which use public libraries as sample libraries, and feature design is required and the robustness of features is not strong, and the accuracy of actual search applications is not high

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