Mining Hard Samples Globally and Efficiently for Person Reidentification

Hao Sheng, Yanwei Zheng, Wei Ke, Dongxiao Yu, Xiuzhen Cheng, Weifeng Lyu, Zhang Xiong

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)

Abstract

Person reidentification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of this task is the hard sample mining. Technically, two strategies could be employed, i.e., global hard mining and local hard mining. For the former, hard samples are mined within the entire training set, while for the latter, it is done in mini-batches. In literature, most existing methods operate locally. Examples include batch-hard sample mining and semihard sample mining. The reason for the rare use of global hard mining is the high computational complexity. In this article, we argue that global mining helps to find harder samples that benefit model training. To this end, this article introduces a new system to: 1) efficiently mine hard samples (positive and negative) from the entire training set and 2) effectively use them in training. Specifically, a ranking list network coupled with a multiplet loss is proposed. On the one hand, the multiplet loss makes the ranking list progressively created to avoid the time-consuming initialization. On the other hand, the multiplet loss aims to make effective use of the hard and easy samples during training. In addition, the ranking list makes it possible to globally and effectively mine hard positive and negative samples. In the experiments, we explore the performance of the global and local sample mining methods, and the effects of the semihard, the hardest, and the randomly selected samples. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.

Original languageEnglish
Article number9035458
Pages (from-to)9611-9622
Number of pages12
JournalIEEE Internet of Things Journal
Volume7
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Convolutional neural networks
  • hard sample mining
  • listwise rank learning
  • multiplet loss
  • person reidentification (ReID)

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