Pedestrian Re-Identification Model Combining Semantic Segmentation and Attention Mechanism

Pedestrian identification results are easily affected by pedestrian posture changes, illumination perspective, background transformation and other factors.To reduce such interference, the existing pedestrian re-identification models usually divide the pedestrians in a dataset into several pieces to extract the local features of the image and improve the identification accuracy, but this also presents new problems such as the mismatch between local features of the human body and the loss of contextual clues of non-human parts.In order to solve the above problems, an improved pedestrian re-identification model is proposed.By aligning the local features of the human semantic parsing network, the semantic segmentation model can perform better in modeling arbitrary contours of pedestrians in the image.The local attention network is also used to capture the lost contextual clues of non-human body parts.The experimental results show that the proposed model displays an average accuracy of 83.5% on Market-1501, 80.8% on DukeMTMC, and 92.4% on CUHK03.The Rank-1 value on the DukeMTMC dataset is 90.2%.Compared with the pedestrian re-identification models based on attention mechanism, pedestrian semantic parsing network or Partial Alignment Network(PAN), the proposed model has higher robustness and mobility.