Identifying a person across cameras in disjoint views at different time and location has important applications in visual surveillance. However, it is difficult to apply existing methods to the development of large-scale person identification systems in practice due to underlying limitations such as high model complexity and batch learning with the labeled training data. In this paper, we propose a prototype system design for large-scale person re-identification that consists of two phases. In order to provide scalability and response within an acceptable time, and handle unlabeled data, we employ an agglomerative hierarchical clustering with simple matching and compact deep neural network for feature extraction.

This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0126-16-1007, Development of Universal Authentication Platform Technology with Context-Aware Multi-Factor Authentication and Digital Signature and No. B0717-16-0107, Development of Video Crowd Sourcing Technology for Citizen Participating-Social Safety Services).

Authors and Affiliations

1. Electronics and Telecommunications Research Institute, Daejon, Republic of Korea

   Seon Ho Oh, Seung-Wan Han, Beom-Seok Choi & Geon-Woo Kim

Corresponding author

Correspondence toSeon Ho Oh.

Editors and Affiliations

1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

   James J. (Jong Hyuk) Park

2. School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

   Shu-Ching Chen

3. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, Adelaide, Australia

   Kim-Kwang Raymond Choo

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