S
Sajid Javed
Researcher at Khalifa University
Publications - 82
Citations - 2564
Sajid Javed is an academic researcher from Khalifa University. The author has contributed to research in topics: Computer science & Background subtraction. The author has an hindex of 19, co-authored 57 publications receiving 1684 citations. Previous affiliations of Sajid Javed include University College of Engineering & Kyungpook National University.
Papers
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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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Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
TL;DR: In this article, the authors provide a magazine-style overview of the entire field of robust subspace learning (RSL) and tracking (RST) for long data sequences, where the authors assume that the data lies in a low-dimensional subspace that can change over time, albeit gradually.
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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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On the Applications of Robust PCA in Image and Video Processing
TL;DR: This paper presents the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing and provides perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.
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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.