Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions

• URL: http://arxiv.org/abs/2002.02603v1
• Date: Fri, 7 Feb 2020 03:18:10 GMT
• Title: Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions
• Authors: Wanxiang Yang, Yan Yan, Si Chen
• Abstract summary: We propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM) The proposed loss enables the deep neural network to adaptively learn discriminative metric embeddings, which significantly improve the capability of recognizing unseen person identities.
• Score: 17.911512103472727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person re-identification (ReID) under occlusions is a challenging problem in video surveillance. Most of existing person ReID methods take advantage of local features to deal with occlusions. However, these methods usually independently extract features from the local regions of an image without considering the relationship among different local regions. In this paper, we propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM), dealing with the problem of occlusions. In particular, we propose a novel loss (termed the adaptive nearest neighbor loss) based on the classification uncertainty to effectively reduce intra-class variations while enlarging inter-class differences within the adaptive neighborhood of the sample. The proposed loss enables the deep neural network to adaptively learn discriminative metric embeddings, which significantly improve the generalization capability of recognizing unseen person identities. Extensive comparative evaluations on challenging person ReID datasets demonstrate the significantly improved performance of the proposed method compared with several state-of-the-art methods.

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