Dynamic Motion And Appearance Modeling For Robust Visual Tracking
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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.
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].
Model Based Visual Tracking
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Author : Giorgio Panin
language : en
Publisher: John Wiley & Sons
Release Date : 2011-04-12
Model Based Visual Tracking written by Giorgio Panin and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-12 with Computers categories.
This book has two main goals: to provide a unifed and structured overview of this growing field, as well as to propose a corresponding software framework, the OpenTL library, developed by the author and his working group at TUM-Informatik. The main objective of this work is to show, how most real-world application scenarios can be naturally cast into a common description vocabulary, and therefore implemented and tested in a fully modular and scalable way, through the defnition of a layered, object-oriented software architecture.The resulting architecture covers in a seamless way all processing levels, from raw data acquisition up to model-based object detection and sequential localization, and defines, at the application level, what we call the tracking pipeline. Within this framework, extensive use of graphics hardware (GPU computing) as well as distributed processing, allows real-time performances for complex models and sensory systems.
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.
Robust Appearance Modeling For Pedestrian
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Author : Summer Newton
language : en
Publisher:
Release Date : 2017-02-03
Robust Appearance Modeling For Pedestrian written by Summer Newton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-03 with categories.
We present an object detection and tracking algorithm thataddresses the problem of multiple simultaneous targets tracking in realworldsurveillance scenarios. The algorithm is based on color changedetection and multi-feature graph matching. The change detector usesstatistical information from each color channel to discriminate betweenforeground and background. Changes of global illumination, dark scenes,and cast shadows are dealt with a pre-processing and post-processingstage. Graph theory is used to find the best object paths across multipleframes using a set of weighted object features, namely color, position,direction and size. The effectiveness of the proposed algorithm and theimprovements in accuracy and precision introduced by the use of multiplefeatures are evaluated on the VACE dataset.
Machine Learning For Vision Based Motion Analysis
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Author : Liang Wang
language : en
Publisher: Springer
Release Date : 2011-04-08
Machine Learning For Vision Based Motion Analysis written by Liang Wang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-08 with Computers categories.
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Coping With Uncertain Dynamics In Visual Tracking
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Author : Leonid Taycher
language : en
Publisher:
Release Date : 2006
Coping With Uncertain Dynamics In Visual Tracking written by Leonid Taycher and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
A model of the world dynamics is a vital part of any tracking algorithm. The observed world can exhibit multiple complex dynamics at different spatio-temporal scales. Faithfully modeling all motion constraints in a computationally efficient manner may be too complicated or completely impossible. Resorting to use of approximate motion models complicates tracking by making it less robust to unmodeled noise and increasing running times. We propose two complimentary approaches to tracking with approximate dynamic models in a probabilistic setting. The Redundant State Multi-Chain Model formalism described in the first part of the thesis allows combining multiple weak motion models, each representing a particular aspect of overall dynamic, in a cooperative manner to improve state estimates. This is applicable, in particular, to hierarchical machine vision systems that combine trackers at several spatio-temporal scales. In the second part of the dissertation, we propose supplementing exploration of the continuous likelihood surface with the discrete search in a fixed set of points distributed through the state space. We demonstrate the utility of these approaches on a range of machine vision problems: adaptive background subtraction, structure from motion estimation, and articulated body tracking.
Visual Tracking Algorithms Using Different Object Representation Schemes
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Author : Shreyamsha Kumar Bidare Kantharajappa
language : en
Publisher:
Release Date : 2019
Visual Tracking Algorithms Using Different Object Representation Schemes written by Shreyamsha Kumar Bidare Kantharajappa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Visual tracking, being one of the fundamental, most important and challenging areas in computer vision, has attracted much attention in the research community during the past decade due to its broad range of real-life applications. Even after three decades of research, it still remains a challenging problem in view of the complexities involved in the target searching due to intrinsic and extrinsic appearance variations of the object. The existing trackers fail to track the object when there are considerable amount of object appearance variations and when the object undergoes severe occlusion, scale change, out-of-plane rotation, motion blur, fast motion, in-plane rotation, out-of-view and illumination variation either individually or simultaneously. In order to have a reliable and improved tracking performance, the appearance variations should be handled carefully such that the appearance model should adapt to the intrinsic appearance variations and be robust enough for extrinsic appearance variations. The objective of this thesis is to develop visual object tracking algorithms by addressing the deficiencies of the existing algorithms to enhance the tracking performance by investigating the use of different object representation schemes to model the object appearance and then devising mechanisms to update the observation models. A tracking algorithm based on the global appearance model using robust coding and its collaboration with a local model is proposed. The global PCA subspace is used to model the global appearance of the object, and the optimum PCA basis coefficients and the global weight matrix are estimated by developing an iteratively reweighted robust coding (IRRC) technique. This global model is collaborated with the local model to exploit their individual merits. Global and local robust coding distances are introduced to find the candidate sample having similar appearance as that of the reconstructed sample from the subspace, and these distances are used to define the observation likelihood. A robust occlusion map generation scheme and a mechanism to update both the global and local observation models are developed. Quantitative and qualitative performance evaluations on OTB-50 and VOT2016, two popular benchmark datasets, demonstrate that the proposed algorithm with histogram of oriented gradient (HOG) features generally performs better than the state-of-the-art methods considered do. In spite of its good performance, there is a need to improve the tracking performance in some of the challenging attributes of OTB-50 and VOT2016. A second tracking algorithm is developed to provide an improved performance in situations for the above mentioned challenging attributes. The algorithms is designed based on a structural local 2DDCT sparse appearance model and an occlusion handling mechanism. In a structural local 2DDCT sparse appearance model, the energy compaction property of the transform is exploited to reduce the size of the dictionary as well as that of the candidate samples in the object representation so that the computational cost of the l_1-minimization used could be reduced. This strategy is in contrast to the existing models that use raw pixels. A holistic image reconstruction procedure is presented from the overlapped local patches that are obtained from the dictionary and the sparse codes, and then the reconstructed holistic image is used for robust occlusion detection and occlusion map generation. The occlusion map thus obtained is used for developing a novel observation model update mechanism to avoid the model degradation. A patch occlusion ratio is employed in the calculation of the confidence score to improve the tracking performance. Quantitative and qualitative performance evaluations on the two above mentioned benchmark datasets demonstrate that this second proposed tracking algorithm generally performs better than several state-of-the-art methods and the first proposed tracking method do. Despite the improved performance of this second proposed tracking algorithm, there are still some challenging attributes of OTB-50 and of VOT2016 for which the performance needs to be improved. Finally, a third tracking algorithm is proposed by developing a scheme for collaboration between the discriminative and generative appearance models. The discriminative model is explored to estimate the position of the target and a new generative model is used to find the remaining affine parameters of the target. In the generative model, robust coding is extended to two dimensions and employed in the bilateral two dimensional PCA (2DPCA) reconstruction procedure to handle the non-Gaussian or non-Laplacian residuals by developing an IRRC technique. A 2D robust coding distance is introduced to differentiate the candidate sample from the one reconstructed from the subspace and used to compute the observation likelihood in the generative model. A method of generating a robust occlusion map from the weights obtained during the IRRC technique and a novel update mechanism of the observation model for both the kernelized correlation filters and the bilateral 2DPCA subspace are developed. Quantitative and qualitative performance evaluations on the two datasets demonstrate that this algorithm with HOG features generally outperforms the state-of-the-art methods and the other two proposed algorithms for most of the challenging attributes.
Computer Vision Accv 2010
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Author : Ron Kimmel
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-14
Computer Vision Accv 2010 written by Ron Kimmel and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-14 with Computers categories.
The four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010. All together the four volumes present 206 revised papers selected from a total of 739 Submissions. All current issues in computer vision are addressed ranging from algorithms that attempt to automatically understand the content of images, optical methods coupled with computational techniques that enhance and improve images, and capturing and analyzing the world's geometry while preparing the higher level image and shape understanding. Novel gemometry techniques, statistical learning methods, and modern algebraic procedures are dealt with as well.
Real Time Vision For Human Computer Interaction
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Author : Branislav Kisacanin
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-08-23
Real Time Vision For Human Computer Interaction written by Branislav Kisacanin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-23 with Computers categories.
The need for natural and effective Human-Computer Interaction (HCI) is increasingly important due to the prevalence of computers in human activities. Computer vision and pattern recognition continue to play a dominant role in the HCI realm. However, computer vision methods often fail to become pervasive in the field due to the lack of real-time, robust algorithms, and novel and convincing applications. This state-of-the-art contributed volume is comprised of articles by prominent experts in computer vision, pattern recognition and HCI. It is the first published text to capture the latest research in this rapidly advancing field with exclusive focus on real-time algorithms and practical applications in diverse and numerous industries, and it outlines further challenges in these areas. Real-Time Vision for Human-Computer Interaction is an invaluable reference for HCI researchers in both academia and industry, and a useful supplement for advanced-level courses in HCI and Computer Vision.