Cross-modal pedestrian re-identification method and system based on distribution space alignment

Abstract

The invention discloses a cross-modal pedestrian re-identification method and system based on distribution space alignment. The method comprises the following steps: constructing a feature extractor and training the feature extractor based on a first training sample; a modal image is acquired, and pedestrian modal features are extracted based on the trained feature extractor; clustering the to-be-detected images according to the modal features of the pedestrians; constructing a modal sharing model, and training the modal sharing model based on the data set with the pseudo label; training a first modal sharing model based on the second training sample; and identifying an input image according to the trained modal sharing model. The system comprises a first construction module, a feature extraction module, a clustering module, a second construction module and an identification module. By using the method of the invention, a cross-modal pedestrian re-identification task can be carried out in two modes of visible light and infrared light. The cross-modal pedestrian re-identification method and system based on distribution space alignment can be widely applied to the field of computer vision.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a cross-modal pedestrian re-identification method and system based on distribution space alignment. Background technique [0002] Single-modal pedestrian re-identification based on visible light is only suitable for scenes with sufficient light. When the light is insufficient, infrared cameras need to be used to take infrared pedestrian pictures. Therefore, it is necessary to solve the problem of visible light-infrared cross-modal pedestrian re-identification, and it is necessary to face visible light images. Problems with large differences from infrared images. Moreover, the supervised model requires a large number of manually labeled cross-modal data sets, and the cost of cross-modal pedestrian identity labeling is much higher than that of single-modality, and the task is more difficult. Contents of the invention [0003] In order to solve the above technical problems, the obj...

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Problems solved by technology

[0002] Single-modal pedestrian re-identification based on visible light is only suitable for scenes with sufficient light. When the light is insufficient, infrared cameras need to be used to take infrared pedestrian pictures. Therefore, it is necessary to solve the problem of visible light-infrared cross-modal pedestrian re-identification, and it is necessary to face visible light images. Problems with large differences from infrared images

And the supervised model requires a large number of cross-modal data sets manually labeled, and the cost of labeling cross-modal pedestrian identities is very high compared with single-modal, and the task is more difficult

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