Gradient descent optimization for visual tracking with ... › publication › fulltext › Gradient-... › publication › fulltext › Gradient-...by Y Dhassi · 2019 · Cited by 2 · Related articlesThis type of treatment is today at the center of many applicatio
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Procedia Computer Science 00 (2019) 000–000 Available online at www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000
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Procedia Computer Science 148 (2019) 164–170
Second International Conference on Intelligent Computing in Data Sciences (ICDS 2018) Second International Conference on Intelligent Computing in Data Sciences (ICDS 2018)
Gradient descent optimization for visual tracking with geometrics Gradient descent optimization for visual tracking with geometrics transformation adaptation transformation adaptation a
Younes Dhassia,* , Samah Elkahaa and Abdellah Aarabaa a,* Younes Dhassi , Samah Elkah and Abdellah Aarab
Laboratory of Electronics Signals Systems and Computers, Faculty of Sciences Dhar- Mahraz Sidi Mohamed Ben Abdellah University, Fes, Morocco Laboratory of Electronics Signals Systems and Computers, Faculty of Sciences Dhar- Mahraz Sidi Mohamed Ben Abdellah University, Fes,
a
Morocco
Abstract Abstract Visual tracking is a fundamental task in many computer vision applications and has been well studied in the last decades. In the field oftracking visual tracking, there are many consider vision which applications make the development of awell robust tracking method very difficult, Visual is a fundamental task inissues manytocomputer and has been studied in the last decades. In the among of thewhich target,make the fast the background cameravery motion, and field of these visualcomplications; tracking, therethe areappearance many issueschange to consider the motion, development of a robustclutter, trackingthemethod difficult, scale To override these problems, change we develop antarget, effective framework for object clutter, trackingthe that addresses mostand of amongvariation. these complications; the appearance of the the general fast motion, the background camera motion, these issues. First the tracking is we formulated in effective the formgeneral of a robust cost function is that a composition of the scale variation. To override theseproblem problems, develop an framework for objectwhich tracking addresses most of appearance dynamic model, problem this formulation ensuresinthethe integration the appearance of thewhich object isand the majority of these issues.and First the tracking is formulated form of of a robust cost function a also composition of the basic geometric transformations. Second the minimization accomplished the gradient descent present appearance and dynamic model, this formulation ensures theisintegration of thebyappearance of the objectoptimization. and also the We majority of experimental results made on different sequences, the experimentations resultsdescent demonstrate the efficiency and basic geometric transformations. Second challenging the minimization is accomplished by the gradient optimization. We present effectiveness of our methods. experimental results made on different challenging sequences, the experimentations results demonstrate the efficiency and effectiveness of our methods. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the the CC scientific committee of (https://creativecommons.org/licenses/by-nc-nd/4.0/) the Second International Conference on Intelligent Computing in This is an open access article under BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the Second International Conference on Intelligent Computing in Data Sciences (ICDS 2018). Peer-review under responsibility of the scientific committee of the Second International Conference on Intelligent Computing in Data Sciences (ICDS 2018). Data Sciences (ICDS 2018). Keywords: Visual tracking ; Gaussian Mixture Model ; Expectation Maximization ; Gradient descent. Keywords: Visual tracking ; Gaussian Mixture Model ; Expectation Maximization ; Gradient descent.
1. Introduction 1. Introduction Visual tracking is an important task within the field of computer vision. The constant increase in the power of computers, the reduction in the costtask of cameras andfield the of increased need for video analysis increase have engendered a keen Visual tracking is an important within the computer vision. The constant in the power of interest in object tracking [1]. Thisand type treatment is today at the centerhave of engendered many applications computers, the reduction in algorithms the cost of cameras theofincreased need for video analysis a keen multimedia in smarttracking visual surveillance, human computer and telerobotics. interest in object algorithms [1]. This type ofinteraction, treatment unmanned is today atvehicles the center of many applications multimedia in smart visual surveillance, human computer interact
