Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이민식 | - |
dc.date.accessioned | 2019-12-11T06:16:17Z | - |
dc.date.available | 2019-12-11T06:16:17Z | - |
dc.date.issued | 2019-11 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v. 30, No. 11, Page. 3260-3274 | en_US |
dc.identifier.issn | 2162-237X | - |
dc.identifier.issn | 2162-2388 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/8626547 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/121230 | - |
dc.description.abstract | This paper proposes a novel unified framework to solve the 3-D localization and tracking problem that occurs multiple camera settings with overlapping views. The main challenge is to overcome the uncertainty of the back projection arising from the challenges of ground point detection in an environment that includes severe occlusions and the unknown heights of people. To tackle this challenge, we establish a Bayesian learning framework that maximizes a posterior over the trajectory assignments and 3-D positions for given detections from multiple cameras. To solve the Bayesian learning problem in a tractable form, we develop an expectation–maximization scheme based on the variation inference approximation, where the probability distributions are designed to follow Boltzmann distributions of seven terms that are induced from multicamera tracking settings. The experimental results show that the proposed method outperforms the state-ofthe-art methods on the challenging multicamera data sets. | en_US |
dc.description.sponsorship | This work was supported in part by MSIP/IITP through the ICT Research and Development Program (Outdoor Surveillance Robots) under Grant 2017-0-00306 and in part by NRF funded by the Ministry of Science and ICT through the Next-Generation ICD Program under Grant 2017M3C4A7077582. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | Trajectory | en_US |
dc.subject | Cameras | en_US |
dc.subject | Target tracking | en_US |
dc.subject | Indexes | en_US |
dc.subject | Bayes methods | en_US |
dc.subject | Estimation | en_US |
dc.subject | Spatiotemporal phenomena | en_US |
dc.subject | 3-D localization and tracking | en_US |
dc.subject | 3-D trajectory estimation | en_US |
dc.subject | multiple cameras | en_US |
dc.subject | multiple target tracking | en_US |
dc.subject | variational inference | en_US |
dc.title | Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras | en_US |
dc.type | Article | en_US |
dc.relation.no | 11 | - |
dc.relation.volume | 30 | - |
dc.identifier.doi | 10.1109/TNNLS.2018.2890526 | - |
dc.relation.page | 3260-3274 | - |
dc.relation.journal | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.contributor.googleauthor | Byeon, M. | - |
dc.contributor.googleauthor | Lee, M. | - |
dc.contributor.googleauthor | Kim, K. | - |
dc.contributor.googleauthor | Choi, J.Y. | - |
dc.relation.code | 2019001539 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DIVISION OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | mleepaper | - |
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