Improving Person Re-Identification with Distance Metric and Attention Mechanism of Evaluation Features

Abstract

In the present study, we developed a person re-identification network called the Multiple Granularity Attention Cosine Network (MGAC). MGAC utilizes the Multiple Granularity Network (MGN), which combines global and local features and constructs an attention mechanism to add to MGN to form a Multiple Granularity Attention Network (MGA). With the attention mechanism, which focuses on important features, MGA assesses the importance of learned features, resulting in higher scores for important features and lower scores for distracting features. Thus, identification accuracy is increased by enhancing important features and ignoring distracting features. We performed experiments involving several classical distance metrics and selected cosine distance as the distance metric for MGA to form the MGAC re-identification network. In experiments on the Market-1501 mainstream dataset, MGAC exhibited high identification accuracies of 96.2% and 94.9% for top-1 and mAP, respectively. The results indicate that MGAC is an effective person re-identification network and that the attention mechanisms and cosine distance can significantly increase the person re-identification accuracy.