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Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras

Title
Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras
Author
이민식
Keywords
Trajectory; Cameras; Target tracking; Indexes; Bayes methods; Estimation; Spatiotemporal phenomena; 3-D localization and tracking; 3-D trajectory estimation; multiple cameras; multiple target tracking; variational inference
Issue Date
2019-11
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v. 30, No. 11, Page. 3260-3274
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.
URI
https://ieeexplore.ieee.org/document/8626547http://repository.hanyang.ac.kr/handle/20.500.11754/121230
ISSN
2162-237X; 2162-2388
DOI
10.1109/TNNLS.2018.2890526
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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