A Pedestrian Re-Identification Method Based on Convolutional Recurrent Network

Abstract

The invention provides a pedestrian re-identification method based on a convolutional cyclic network. The pedestrian re-identification method based on the convolutional recurrent network includes: acquiring cross-camera pedestrian images, constructing a pedestrian re-identification training data set, the data set including a preset number of pedestrian images; constructing a pedestrian feature extraction convolution loop network, the network can extract the hash binary vector features of preset dimensions; construct a pedestrian re-identification twin network, and design an optimization objective function for the paired features; use the training data set to train the twin network, and obtain the pedestrian re-identification twin network. Identify the parameters of the feature extraction network model; use the feature extraction network to separately extract the cross-camera pedestrian image features; calculate the cross-camera pedestrian feature similarity, and complete the pedestrian cross-camera re-identification problem according to the similarity.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a pedestrian re-identification method based on a convolutional cyclic network. Background technique [0002] The progress and development of society pay more and more attention to public safety, and the video surveillance technology developed with it is widely used in public transportation and office places. People judge whether there is danger by watching and browsing surveillance video content, or use surveillance to pursue accountability after the event, etc. , but currently the use of video technology mainly depends on people, especially the need to find the same pedestrian across cameras, and completing the trajectory correlation analysis of pedestrians is one of the requirements of the application. Cross-camera pedestrian recognition is a pedestrian re-identification problem. Due to the influence of different cameras on shooting angles, illumination, occlusion, ...

AI Technical Summary

Problems solved by technology

Pedestrian feature representation has experienced early color and texture HOG (Histogram of Gradient) features, to more effective LOMO (Local Maximal Occurrence), fusion features, and feature space transformation mainly includes XQDA (Cross-viewQuadratic Discriminant Analysis), NFST (NullFoley -Sammon Transfer) and other methods, but these methods are basically designed manually and implemented step by step, and the effect is not ideal

In recent years, deep learning technology has also been well developed in the field of pedestrian re-identification, but the current research mainly uses convolutional networks to extract pedestrian image features. Discovery and mining of image structure information

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