Strong Feature Fusion Networks for Person Re-Identification

Abstract

<p indent=0mm>With the development of deep learning, the performance of person Re-Identification (Re-ID) has been significantly improved. It’s still a challenging task due to the challenges coming from large variations on persons such as occlusion, background clutter, pose, illumination and detection failure, etc. To retrieve true pedestrians, robust feature expression is significant. Instead of using external cues, this paper takes advantage of robust alignment features and metric learning. First, from the aspect of feature extraction, there were three contributions. (i) Embeded a spatial transformer network in the network architecture, which is called ResNet_STN in this paper, which can solve the problem of local spatial semantic feature inconsistency, accurately express the main characteristics of the target, and achieve pedestrian alignment. (ii) Designed a strong feature fusion network based on the aligned local features, which is named a Strong Feature Fusion Module (SFFM) and can make full use of the connection between semantic information to extract detailed features of images. Then, from the aspect of metric loss function, one contribution was put forward. (iii) Proposed a Ranking Matrix (RM) method to select local triplet samples and compute local triplet loss. We combined a regularized classification loss to train the network to unleash the discrimination ability of the learned strong representations of this network architecture. Finally, the proposed method with the existing re-ranking algorithm to further improves Rank-1 and mAP retrieval accuracy. Experimental results on Market-1501 dataset demonstrate the effectiveness of our proposed method.