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Occluded Re-ID task is proposed mainly because people are often occluded by various obstacles in the real world, which greatly affects the accuracy of model matching.In view of the challenge of occluded Re-ID, the main work of this paper is as follows:(i) Aiming at the incompleteness of human body under occlusion, an occluded Re-ID method based on multi-scale features is proposed. A partial human body locator is constructed by using the target detection algorithm to automatically recognize and cut partial human body in this method. Then this method designs a horizontal pyramid pooling strategy to extract multi-scale features and enhance the robustness of the model under the occlusion problem. Experiments show that, this method has better matching accuracy in the occluded Re-ID task. (ii) Aiming at the problem that it is difficult to align the local features between different people images under occlusion, an occluded Re-ID method based on human feature reconstruction is proposed. This method is an unaligned method, which uses sparse representation to reconstruct human body features. Difficult sample triplet loss function was improved by using human feature reconstruction distance and the proportion of similar parts to matching correlation was increased. Experiments show that this method can effectively improve the occlusion resistance of the model.

Occluded Person Re-Identification Method Based on Multiscale Features and Human Feature Reconstruction