MULTI-GRANULARITY CLOTHING CHANGING-SPECIFIC PERSON RE-IDENTIFICATION (REID) METHOD BASED ON CLOTHING DESENSITIZATION NETWORK

The present disclosure provides a multi-granularity clothing changing- specific person re-identification (ReID) method based on a clothing desensitization network. To resolve a problem caused when a person changes clothing, a clothing desensitization network is used to learn a generalized appearance feature of the person, so that a model can identify the person without relying on the appearance features such as clothing color and texture. A loss is calculated based on a pre-generated part semantic segmentation image and a feature map to assist feature alignment. This can prevent similarity measurement on a background and a half of the body to a greatest extent, and eliminate negative optimization. According to the method in the present disclosure, a coarse-to-fine grained multi-level training method is used for training. In this way, more effective attribute information can be extracted compared with attribute information extracted based on a single global feature. The multi-granularity clothing changing-specific person ReID method based on a clothing desensitization network in the present disclosure achieves an excellent effect in relevant data sets for clothing changing-specific person RelD.