Person Re-Identification with Improved Performance by Incorporating Focal Tversky Loss in AGW Baseline

Shao Kang Huang, Chen Chien Hsu*, Wei Yen Wang

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)

摘要

Person re-identification (re-ID) is one of the essential tasks for modern visual intelligent systems to identify a person from images or videos captured at different times, viewpoints, and spatial positions. In fact, it is easy to make an incorrect estimate for person re-ID in the presence of illumination change, low resolution, and pose differences. To provide a robust and accurate prediction, machine learning techniques are extensively used nowadays. However, learning-based approaches often face difficulties in data imbalance and distinguishing a person from others having strong appearance similarity. To improve the overall re-ID performance, false positives and false negatives should be part of the integral factors in the design of the loss function. In this work, we refine the well-known AGW baseline by incorporating a focal Tversky loss to address the data imbalance issue and facilitate the model to learn effectively from the hard examples. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% (with mAP: 94.5) and rank-1 accuracy of 93% (with mAP: 91.4) on Market1501 and DukeMTMC datasets, respectively, outperforming the state-of-the-art approaches.

原文英語
文章編號9852
期刊Sensors
22
發行號24
DOIs
出版狀態已發佈 - 2022 12月

ASJC Scopus subject areas

  • 分析化學
  • 資訊系統
  • 生物化學
  • 原子與分子物理與光學
  • 儀器
  • 電氣與電子工程

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