Occluded pedestrian re-identification method based on massed learning and deep network learning

Abstract

The invention discloses an occluded pedestrian re-identification method based on massed learning and deep network learning. According to the method, multiple types of occluded training samples are generated from original non-occluded training samples through an occlusion simulator, the generated occluded training samples and the original training samples form a united training set used for model training, meanwhile, occluded and non-occluded classification losses are added into pedestrian classification losses, and a multi-task loss function is used to replace a single-task loss function in the past. In this way, pedestrian re-identification under occlusion is effectively coped with, and priori information of occlusion and non-occlusion is considered to perform feature extraction during deep network feature learning. Experiments indicate that through the method, the performance of an existing deep network in occluded pedestrian re-identification can be substantially improved, and the method has high application value.

Description

technical field [0001] The present invention relates to a pedestrian re-identification method for occlusion problems, and more specifically, to a occluded pedestrian re-identification method based on centralized learning and deep network learning. Background technique [0002] The task of pedestrian re-identification is to identify the same target object that appears in another camera under one camera. Among them, the occlusion problem is an urgent problem to be solved in pedestrian re-identification. Pedestrian occlusion generally occurs in crowded or complex construction scenes, and these scenes are often accident-prone. For example, a suspect in a dense area may be blocked by pedestrians or other objects such as cars, luggage, and street signs. In this case, the camera captures images of pedestrians with occlusions. We need to search for this complete pedestrian in the pedestrian library or other cameras, which is the task of occluded pedestrian re-identification. There...

AI Technical Summary

Problems solved by technology

[0004] Although the research work on pedestrian re-identification has been well developed, the existing technology almost does not consider occlusion for pedestrian re-identification, and rarely builds models for occluded pedestrian images.

The occlusion part brings interference to the extracted image features, resulting in poor re-identification of pedestrians who encounter occlusion in practical applications

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