S
Soon Ki Jung
Researcher at Kyungpook National University
Publications - 151
Citations - 2652
Soon Ki Jung is an academic researcher from Kyungpook National University. The author has contributed to research in topics: Background subtraction & Augmented reality. The author has an hindex of 23, co-authored 151 publications receiving 2151 citations. Previous affiliations of Soon Ki Jung include KAIST & University of Southern California.
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Deep neural network concepts for background subtraction:A systematic review and comparative evaluation
TL;DR: In this article, the authors provide a review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions.
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Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
TL;DR: A rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.
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Decomposition into low-rank plus additive matrices for background/foreground separation
TL;DR: In this paper, a comprehensive review of the robust subspace learning and tracking frameworks for background/foreground separation is presented, with a focus on the specificities of the background and the foreground as well as the temporal and spatial properties of the problem formulation.
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Background–Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering
TL;DR: This work proposes to incorporate the spatial and temporal sparse subspace clustering into the robust principal component analysis (RPCA) framework and demonstrates excellent performance of the proposed algorithm for both the background estimation and foreground segmentation.
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Spatiotemporal Low-Rank Modeling for Complex Scene Background Initialization
TL;DR: A spatiotemporal low-rank modeling method on dynamic video clips for estimating the robust background model and superior performance over state-of-the-art approaches is demonstrated.