Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks
| Autor: | Yeong-Jun Cho, Jae-Han Park, Su-A Kim, Kuk-Jin Yoon, Kyuewang Lee |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Reliability (computer networking) Computer Vision and Pattern Recognition (cs.CV) 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Topology inference Topology (electrical circuits) 02 engineering and technology computer.software_genre Network topology Re identification Task (computing) Camera network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer 021101 geological & geomatics engineering |
| Zdroj: | ICCV Workshops |
| Popis: | Person re-identification in large-scale multi-camera networks is a challenging task because of the spatio-temporal uncertainty and high complexity due to large numbers of cameras and people. To handle these difficulties, additional information such as camera network topology should be provided, which is also difficult to automatically estimate. In this paper, we propose a unified framework which jointly solves both person re-id and camera network topology inference problems. The proposed framework takes general multi-camera network environments into account. To effectively show the superiority of the proposed framework, we also provide a new person re-id dataset with full annotations, named SLP, captured in the synchronized multi-camera network. Experimental results show that the proposed methods are promising for both person re-id and camera topology inference tasks. Accepted to International Workshop on Cross-domain Human Identification (in conjunction with ICCV), 2017 |
| Databáze: | OpenAIRE |
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