A method for person re-identification based on video-based synchronous enhancement of appearance and motion information

Abstract

The invention discloses a pedestrian re-identification method based on synchronous enhancement of video appearance and motion information. During training, two modules, an appearance enhancement module AEM and a motion enhancement module MEM, respectively enhance the pedestrian appearance and motion information in a backbone network. The appearance enhancement module AEM uses the attribute recognition model trained by the existing large-scale pedestrian attribute datasets to provide attribute pseudo-labels for large-scale pedestrian video datasets, and enhances appearance and semantic information through attribute learning; the motion enhancement module MEM uses video The prediction model predicts the gait information of pedestrians, enhances the gait information features with identity discrimination in the backbone network of pedestrian feature extraction, and improves the performance of pedestrian re-identification. In practical applications, only the backbone network for pedestrian feature extraction needs to be retained, and higher pedestrian re-identification performance can be obtained without increasing network complexity and model size. The enhanced backbone features achieve higher accuracy in video-based person re-identification tasks.

Description

technical field [0001] The invention belongs to the technical field of intelligent identification, in particular to a pedestrian re-identification method based on synchronous enhancement of video appearance and motion information. In the part of algorithm design and model training, deep learning technology is involved. Background technique [0002] The task of pedestrian re-identification is to retrieve the pedestrian target in camera B where there is no overlapping area of ​​camera A, and find the pedestrian target appearing in camera A again. As an important research direction and research hotspot at present, pedestrian re-identification has a wide range of applications in the fields of intelligent monitoring, smart city, public security prevention and criminal investigation, such as cross-camera pedestrian tracking and behavior analysis, and image retrieval of suspects or interested persons. with queries etc. [0003] With the rapid development of deep learning, more an...

AI Technical Summary

Problems solved by technology

This method adopts a deep neural network model, uses attribute learning and gait prediction to enhance the appearance information and motion information in the pedestrian feature extraction backbone network, and solves the problem of insufficient utilization of video data information by the pedestrian feature extraction backbone network in the existing methods problems, and fully improve the performance of backbone network feature extraction and pedestrian re-identification

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