Person re-identification based on multi-scale global feature and weight-driven part feature

Abstract

Person re-identification (ReID) is a crucial task in identifying pedestrians of interest across multiple surveillance camera views. ReID methods in recent years have shown that using global features or part features of the pedestrian is extremely effective, but many models do not have further design models to make more reasonable use of global and part features. A new model is proposed to use global features more rationally and extract more fine-grained part features. Specifically, our model captures global features by using a multi-scale attention global feature extraction module, and we design a new context-based adaptive part feature extraction module to consider continuity between different body parts of pedestrians. In addition, we have added additional enhancement modules to the model to enhance its performance. Experiments show that our model achieves competitive results on the Market1501, Dukemtmc-ReID, and MSMT17 datasets. The ablation experiments demonstrate the effectiveness of each module of our model. The code of our model is available at: https://github.com/davidtqw/Person-Re-Identification.

Keywords

Person re-identification global feature part feature multi-scaler weight-driven non-local mudule

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