Person re-identification based on frequency channel attention networks under the surveillance scenario

Abstract

Under the surveillance scenario, due to the influence of occlusion, deformation and illumination, the accuracy of person re-identification (ReID) task will be greatly affected. The extraction of effective pedestrian features has become the key of person ReID task. In view of this problem, this paper creatively uses frequency channel attention (FCA) network to carry out person ReID task. FCA network solves the problem of insufficient information representation caused by global average pooling (GAP) in traditional channel attention network. In addition, this paper conducts experiments on two datasets. The ablation experimental results show the effectiveness of FCA network in person ReID task, and the results of contrastive experiment show the superiority of FCA network in person ReID task.