Research on Improved SeqNet for Person Search

Person search aims at finding the target person from the panoramic image with the multiple camera views. It can be widely used in many areas such as public security and intelligent video surveillance. However, person search is a challenging task due to the cluttered background, occlusion, and person pose variations. We present a person search algorithm based on improved SeqNet to address these challenges. On the basis of SeqNet, an attention mechanism is utilized to extract global features and local features, which are fused with the features extracted by SeqNet to enhance the discrimination of person features. In addition, a feature update strategy is designed based on IoU groups to solve the problem of gradually weakening historical features during the feature update process. The comprehensive experiments are executed on CUHK-SYSU and PRW datasets, the results show that our proposed method is effective and superior to most existing works.