On Learning Models Of Appearance For Robust Long Term Visual Navigation
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On Learning Models Of Appearance For Robust Long Term Visual Navigation
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Author : Lee Eric Clement
language : en
Publisher:
Release Date : 2020
On Learning Models Of Appearance For Robust Long Term Visual Navigation written by Lee Eric Clement and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Simultaneous localization and mapping (SLAM) is a class of techniques that allow robots to navigate unknown environments using onboard sensors. With inexpensive commercial cameras as the primary sensor, visual SLAM has become an important and widely used approach to enabling mobile robot autonomy. However, traditional visual SLAM algorithms use only a fraction of the information available from conventional cameras: in addition to the basic geometric cues typically used in visual SLAM, colour images encode information about the camera itself, environmental illumination, surface materials, vehicle motion, and other factors influencing the image formation process. Moreover, visual localization performance degrades quickly in long-term deployments due to environmental appearance changes caused by lighting, weather, or seasonal effects. This is especially problematic when continuous metric localization is required to drive vision-in-the-loop systems such as autonomous route following. This thesis explores several novel approaches to exploiting additional information from cameras to improve the accuracy and reliability of metric visual SLAM algorithms in short- and long-term deployments. First, we develop a technique for reducing drift error in visual odometry (VO) by estimating the position of a known light source such as the sun using indirect illumination cues available from existing image streams. We build and evaluate hand-engineered and learned models for single-image sun detection and achieve significant reductions in drift error over 30~km of driving in urban and planetary analogue environments. Second, we explore deep image-to-image translation as a means of improving metric visual localization under time-varying illumination. Using images captured under different illumination conditions in a common environment, we demonstrate that localization accuracy and reliability can be substantially improved by learning a many-to-one mapping to a user-selected canonical appearance condition. Finally, we develop a self-supervised method for learning a canonical appearance optimized for high-quality localization. By defining a differentiable surrogate loss function related to the performance of a non-differentiable localization pipeline, we train an optimal RGB-to-grayscale mapping for a given environment, sensor, and pipeline. Using synthetic and real-world long-term vision datasets, we demonstrate significant improvements in localization performance compared to standard grayscale images, enabling continuous metric localization over day-night cycles using a single mapping experience.
Online Adaptive Appearance Models For Robust Visual Tracking
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Author : S. M. Shahed Nejhum
language : en
Publisher:
Release Date : 2011
Online Adaptive Appearance Models For Robust Visual Tracking written by S. M. Shahed Nejhum and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
ABSTRACT: Robust tracking of visual targets is a very challenging task in the field of computer vision. The target has to be reliably modeled and the model needs to be updated according to the target's appearance and shape variations over time. Visual tracking algorithms available in the literature do not fully explore mid-level image cues. This dissertation presents visual tracking algorithms where mid-level image cues are used efficiently and effectively to model the target. The first algorithm tracks articulated objects by constantly modeling the changing target shape by a small number of rectangular blocks whose positions are updated accordingly. To improve the tracking speed a modified algorithm processes the computationally extensive steps in parallel using a GPU. Both algorithms are evaluated on several videos of articulated targets undergoing significant shape variations. We compare the results with the mean shift [1] tracker and the histogram-based tracker [2]. Our algorithms consistently outperform these algorithms [1, 2] and produce robust tracking results. We present a novel technique to generate coherent superpixels from a pair of successive video frames. We show that the similarity of corresponding superpixels can be increased by generating superpixels jointly from the images. We present a visual tracking algorithm that uses a novel superpixel-based appearance model. The model is continuously updated to handle variations of the target. To evaluate the performance of the tracker, we report experimental results on several publicly available challenging sequences. We show that our superpixel-based visual tracker produces improved performance over recently published state-of-the-art tracking algorithms [3-5].
Robust Online Appearance Models For Visual Tracking Microform
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Author : Thomas F. (Thomas Farid) El-Maraghi
language : en
Publisher: National Library of Canada = Bibliothèque nationale du Canada
Release Date : 2003
Robust Online Appearance Models For Visual Tracking Microform written by Thomas F. (Thomas Farid) El-Maraghi and has been published by National Library of Canada = Bibliothèque nationale du Canada this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
Pattern Recognition
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Author : Shutao Li
language : en
Publisher: Springer
Release Date : 2014-11-05
Pattern Recognition written by Shutao Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-05 with Computers categories.
The two-volume set CCIS 483 and CCIS 484 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition, CCPR 2014, held in Changsha, China, in November 2014. The 112 revised full papers presented in two volumes were carefully reviewed and selected from 225 submissions. The papers are organized in topical sections on fundamentals of pattern recognition; feature extraction and classification; computer vision; image processing and analysis; video processing and analysis; biometric and action recognition; biomedical image analysis; document and speech analysis; pattern recognition applications.
Online Appearance Based Place Recognition And Mapping
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Author : Konstantinos A. Tsintotas
language : en
Publisher: Springer Nature
Release Date : 2022-09-01
Online Appearance Based Place Recognition And Mapping written by Konstantinos A. Tsintotas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-01 with Technology & Engineering categories.
This book introduces several appearance-based place recognition pipelines based on different mapping techniques for addressing loop-closure detection in mobile platforms with limited computational resources. The motivation behind this book has been the prospect that in many contemporary applications efficient methods are needed that can provide high performance under run-time and memory constraints. Thus, three different mapping techniques for addressing the task of place recognition for simultaneous localization and mapping (SLAM) are presented. The book at hand follows a tutorial-based structure describing each of the main parts needed for a loop-closure detection pipeline to facilitate the newcomers. It mainly goes through a historical review of the problem, focusing on how it was addressed during the years reaching the current age. This way, the reader is initially familiarized with each part while the place recognition paradigms follow.
Visual Object Tracking Using Deep Learning
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Author : Ashish Kumar
language : en
Publisher: CRC Press
Release Date : 2023-11-10
Visual Object Tracking Using Deep Learning written by Ashish Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-10 with Technology & Engineering categories.
This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods, various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also: Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methods. Elaborates on the salient features of deep learning trackers along with traditional trackers, wherein the handcrafted features are fused to reduce computational complexity. Illustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenarios. Explores the future research directions for visual tracking by analyzing the real-time applications. The book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques, evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.
Dynamic Motion And Appearance Modeling For Robust Visual Tracking
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Author : Hwasup Lim
language : en
Publisher:
Release Date : 2007
Dynamic Motion And Appearance Modeling For Robust Visual Tracking written by Hwasup Lim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Sparse Representation Based Online Appearance Models For Robust Visual Tracking
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Author : 白天翔
language : en
Publisher:
Release Date : 2012
Sparse Representation Based Online Appearance Models For Robust Visual Tracking written by 白天翔 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Automatic tracking categories.
Computer Vision Eccv 2014
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Author : David Fleet
language : en
Publisher: Springer
Release Date : 2014-08-14
Computer Vision Eccv 2014 written by David Fleet and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-14 with Computers categories.
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Methods For Appearance Based Loop Closure Detection
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Author : Emilio Garcia-Fidalgo
language : en
Publisher: Springer
Release Date : 2018-03-14
Methods For Appearance Based Loop Closure Detection written by Emilio Garcia-Fidalgo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-14 with Technology & Engineering categories.
Mapping and localization are two essential tasks in autonomous mobile robotics. Due to the unavoidable noise that sensors present, mapping algorithms usually rely on loop closure detection techniques, which entail the correct identification of previously seen places to reduce the uncertainty of the resulting maps. This book deals with the problem of generating topological maps of the environment using efficient appearance-based loop closure detection techniques. Since the quality of a visual loop closure detection algorithm is related to the image description method and its ability to index previously seen images, several methods for loop closure detection adopting different approaches are developed and assessed. Then, these methods are used in three novel topological mapping algorithms. The results obtained indicate that the solutions proposed attain a better performance than several state-of-the-art approaches. To conclude, given that loop closure detection is also a key component in other research areas, a multi-threaded image mosaicing algorithm is proposed. This approach makes use of one of the loop closure detection techniques previously introduced in order to find overlapping pairs between images and finally obtain seamless mosaics of different environments in a reasonable amount of time.