Journal ArticleDOI
Person Re-Identification by Iterative Re-Weighted Sparse Ranking
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TLDR
The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration of an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets.Abstract:
In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.read more
Citations
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Person Re-identification: Past, Present and Future
TL;DR: The history of person re-identification and its relationship with image classification and instance retrieval is introduced and two new re-ID tasks which are much closer to real-world applications are described and discussed.
Proceedings ArticleDOI
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification
TL;DR: In this article, a Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) model is proposed to simultaneously learn an attribute-semantic and identity discriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain.
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Learning a Discriminative Null Space for Person Re-identification
Li Zhang,Tao Xiang,Shaogang Gong +2 more
TL;DR: This work proposes to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data, which has a fixed dimension, a closed-form solution and is very efficient to compute.
Proceedings ArticleDOI
Learning a Discriminative Null Space for Person Re-identification
Li Zhang,Tao Xiang,Shaogang Gong +2 more
TL;DR: In this paper, images of the same person are collapsed into a single point, thus minimising the within-class scatter to the extreme and maximising the relative between-class separation simultaneously.
Journal ArticleDOI
A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification
TL;DR: The experimental results demonstrate that the bottom-up clustering approach to jointly optimize a convolutional neural network and the relationship among the individual samples is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably than competing transfer learning and semi-supervised learning methods.
References
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Enhancing Sparsity by Reweighted ℓ 1 Minimization
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Pictorial Structures for Object Recognition
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