A method for generating pedestrian re-identification data under different illumination conditions based on an adversarial network

Abstract

The invention discloses a method for generating pedestrian re-identification data under different illumination conditions based on an adversarial network. The method comprises the following steps: firstly, acquiring videos of different time periods in a monitoring video, namely monitoring videos under different illumination conditions; Extracting representative monitoring images from the acquiredmonitoring videos, and marking the representative monitoring images into three different categories of'too dark, normal and too bright 'according to illumination conditions; Sending the images with the illumination condition labeling information into a generative adversarial network for training, wherein the trained generative network is used for generating data under different illumination conditions; And generating the pedestrian re-identification data into data under different illumination conditions through the trained generation network; And finally, adding the obtained data as a data augmentation form into pedestrian re-identification model training, and at the moment, enabling the model to identify the characteristics of the same target under different illumination conditions, i.e.,enhancing the identification performance of the model on the target under different illumination conditions.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to a method for generating pedestrian re-identification data under different illumination conditions based on an adversarial network. Background technique [0002] Pedestrian re-identification refers to the recognition of the same target pedestrian across cameras. In particular, under the current situation of rapid development of deep learning theory, pedestrian re-identification mainly relies on intelligent video analysis algorithms based on deep learning to automatically realize. This technology has important implications for public safety. In practice, all the pedestrians in the surveillance video to be analyzed are first intercepted by the pedestrian detection algorithm, and then each intercepted pedestrian is used as input, and is mapped to the corresponding feature vector (or feature vector) by a convolutional neural network. Figure), at this time, the pedestrian re-ide...

AI Technical Summary

Problems solved by technology

[0006] However, the apparent features are very susceptible to changes in the image due to illumination

For example, when a pedestrian wearing dark clothes walks into the shadow, the color of his

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