Group pedestrian re-identification method based on hybrid attention mechanism

Abstract

The invention provides a group pedestrian re-identification method based on a hybrid attention mechanism. The method comprises the following steps: carrying out preliminary feature extraction on a group image by using a deep convolutional neural network backbone model; further extracting the preliminarily extracted features by using a hybrid attention mechanism model; and comparing and evaluatingthe features of the mixed attention model by using a least square residual distance. According to the invention, various challenges existing in a group pedestrian re-identification problem are fully considered; a hybrid attention model including spatial attention and channel attention is utilized, so that the network pays more attention to key areas and features of group images, a novel least square residual distance based on a least square algorithm is proposed, and measurement between group image pairs is learned better. The method can well adapt to various challenges brought by group pedestrian images, and has good diversity and general applicability.

Description

technical field [0001] The invention belongs to the technical field of computer vision, in particular to a group pedestrian re-identification method based on a mixed attention mechanism, and relates to group-focused pedestrian re-identification under non-overlapping surveillance cameras. Background technique [0002] In recent years, people have paid more and more attention to public safety issues, and the degree of attention to safety issues has risen to a new height. The video surveillance network in the city is becoming more and more perfect, and video surveillance is spread all over the city. The quality and quality of video surveillance data The number has greatly increased. In all parts of the video surveillance network, considering the flexibility and variability of pedestrian activities and the significance of monitoring pedestrians for protecting personnel safety and criminal investigation and solving crimes, pedestrians are one of the key objects of surveillance vi...

AI Technical Summary

Problems solved by technology

Some works extract subsets in the group and use multi-level matching algorithm to iteratively match, which can fully extract the characteristics of the group and can deal with the change of the number of people in the group (see Xiao, Hao, et al. "Group Re-Identification: Leveraging and Integrating Multi-Grain Information."2018 ACMMultimedia Conference on Multimedia Conference.ACM,2018.), but the matching process is very time-consuming and not suitable for large data sets and real-world scenarios

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