Beyond triplet loss: a deep quadruplet network for person re-identification

Abstract

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.

accepted to CVPR2017

References in corpus (2)

• Caffe: Convolutional Architecture for Fast Feature Embedding
• Person Re-identification: Past, Present and Future

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• Re-Identification with Consistent Attentive Siamese Networks
• View Confusion Feature Learning for Person Re-identification
• Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
• Unity Style Transfer for Person Re-Identification
• Neural Signatures for Licence Plate Re-identification
• A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification
• Channel Interaction Networks for Fine-Grained Image Categorization
• A Strong Baseline for Fashion Retrieval with Person Re-Identification Models
• Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification
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