Research on Pedestrian Re-identification Method Based on Multi-granularity Features
Due to interference factors such as occlusion,low image resolution,and changes in person poses in the collected images,the research on person re-identification is extremely challenging.To this end,this paper proposes a pedestrian re-identification network based on Attention Mechanism and multi-granularity features.Firstly,in response to the change of pedestrian posture,this paper designs a multi-granularity feature extraction module,which uses a multi-branch network joint Attention Mechanism to extract multi-level global features and local features.Secondly,for the pedestrian local misalignment problem,this paper proposes a neighborhood adaptive feature fusion module.In addition,in order to retain more useful information,this paper also designs an adaptive feature pooling module.It conducts experiments on two public data sets,and the comparison results with other methods verify the effectiveness of the proposed method.
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