Proceedings ArticleDOI
Scalable Person Re-identification: A Benchmark
Liang Zheng,Liang Zheng,Liyue Shen,Lu Tian,Shengjin Wang,Jingdong Wang,Qi Tian +6 more
- pp 1116-1124
TLDR
A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.Abstract:
This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.read more
Citations
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Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
TL;DR: In this paper, a part-based convolutional baseline (PCB) is proposed to learn discriminative part-informed features for person retrieval and two contributions are made: (i) a network named Part-based Convolutional Baseline (PCBB) which outputs a convolutionAL descriptor consisting of several part-level features.
Proceedings ArticleDOI
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TL;DR: A Person Transfer Generative Adversarial Network (PTGAN) is proposed to relieve the expensive costs of annotating new training samples and comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
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