Investigating fast re-identification for multi-camera indoor person tracking. (July 2019)

• Record Type: Journal Article
• Authors: Chen, Andrew Tzer-Yeu; Biglari-Abhari, Morteza; Wang, Kevin I-Kai
• Is Part Of: Computers & electrical engineering. Volume 77(2019)
• Page Start: 273
• Page End: 288
• Publication Date: 2019-07
• Subjects: Computer vision -- Person tracking -- Person re-identification -- Metric learning -- Classification -- Camera surveillance; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.06.009
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In person tracking applications involving multiple cameras, person re-identification is an important step for ensuring accurate tracking of individuals as they move between camera views. However, changes in camera parameters and environmental conditions can make re-identification challenging. This is especially difficult in resource-constrained environments, as is often the case in many real-world intelligent applications. In this paper, we explore dimensionality reduction, metric learning, and classification for achieving re-identification in a computationally efficient way. We report that the covariance metric transformation is a sufficient distance metric for achieving good linear separability between identity classes, and produces better results than more complex approaches across two re-identification datasets. We also explore one-shot learning methods for performing classification, show that our Sequential k-Means algorithm outperforms other fast one-shot learning approaches, and discuss parameter tuning to improve accuracy.