Pedestrian re-identification method based on multi-channel attention characteristics

Abstract

The invention discloses a pedestrian re-identification method based on multi-channel attention characteristics, which comprises the following steps: 1) constructing a convolutional neural network model based on channel attention, and pre-training a trunk network; 2) extracting output characteristics of the pedestrian picture in the trunk network, and calculating channel weighted vectors of the characteristics after global pooling; 3) multiplying the weighted vector by the output characteristic of the main network, and adding the multiplied weighted vector to obtain a channel attention characteristic; 4) repeatedly extracting a plurality of attention characteristics, and performing characteristic diversity regularization by adopting a Hailinger distance; 5) inputting the attention characteristics into a full connection layer and a classifier, and performing training to minimize cross entropy loss and metric loss; and 6) inputting the test set pictures into the trained model to extract features, and realizing pedestrian re-identification through metric sorting. According to the pedestrian re-identification method based on the attention mechanism, discriminative features of pedestrians are extracted based on the attention mechanism, repeated extraction of similar attention features is limited, and the accuracy and robustness of pedestrian re-identification are effectively improved.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and computer vision, in particular to a pedestrian re-identification method based on multi-channel attention features. Background technique [0002] With the acceleration of urbanization, the demand for social public security is increasing. Many important public places are covered by extensive camera networks. The use of computer vision technology to automate monitoring has become a hot spot of attention, and pedestrian re-identification technology has gradually become the focus of research. In general, given an image or video clip of a target pedestrian, person re-identification is used to retrieve the target pedestrian across the camera field of view. Due to the complexity and changeability of the monitoring scene, the collected pedestrian images often have difficulties such as illumination changes, perspective posture changes, and occlusions, which bring great challenges to ped...

AI Technical Summary

Problems solved by technology

Due to the inability of horizontal segmentation to accurately locate highly discriminative local parts, the accuracy performance is limited

In addition, the method using local part detection requires additional location labels, and the additional labeling work consumes manpower and material resources, which reduces the practicability of the method

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