Abstract
Person re-identification (re-id) plays a vital role in surveillance and forensics application. Since the labeled images for person re-id task is limited, the generalization ability of existed person re-id models is poor. On the other hand, images of different classes (pedestrian and non-pedestrian images) share some general features. To this end, this paper aims to improve the performance of person re-id by designing a relearning network which can learn domain-specific features and general features simultaneously. The proposed relearning network consists of a pretrained backbone network which provides the general features, and several attention-based subnetworks that learn domain-specific features from general features of different levels. Besides, we propose a coarse-fine loss to improve the generalization of person re-id model by making full use of the massive labeled non-pedestrian images. Experimental results on the publicly available Market-1501, DukeMTMC-reID and CUHK03 pedestrian re-id datasets demonstrate the effectiveness of the proposed relearning network and coarse-fine loss.
Highlights
P ERSON re-identification is a crucial technique in automated surveillance and forensics application
In this paper, we focus on designing a network to learn domain-specific features for person re-id task from the general features exploited from large-scale labeled non-pedestrian datasets
The Effect of Relearning Network: Since the key difference between our relearning network and the backbone network is the three subnetworks listed in Table 1, we study the effect of the relearning network by attaching these three subnetworks one by one to the backbone network
Summary
P ERSON re-identification (re-id) is a crucial technique in automated surveillance and forensics application. Given an image of target pedestrian (probe), the task of person re-id aims at retrieving images of the same pedestrian in a large set of candidate images (gallery) captured by different nonoverlapping cameras. The person re-id problem has attracted lots of research attention during the past decades [5], [15], [21], [24], [26], [42], [46], [51], [53]. Existing works solve the person re-id problem from two perspectives. The first one focuses on developing discriminative feature representation for each image of pedestrian. Deep learning, which can learn discriminative feature representation and powerful distance metric simultaneously, has achieved great success in many fields of computer vision, such as, image classification/retrieval [13],
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