Abstract
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.
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Acknowledgments
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).
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Oh, S.H., Han, SW., Choi, BS., Kim, GW. (2017). Prototype System Design for Large-Scale Person Re-identification. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_103
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DOI: https://doi.org/10.1007/978-981-10-5041-1_103
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