Multiple Object Tracking With Occlusion Handling
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Multiple Object Tracking With Occlusion Handling
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Author : Murtaza Safri
language : en
Publisher:
Release Date : 2010
Multiple Object Tracking With Occlusion Handling written by Murtaza Safri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Object tracking is an important problem with wide ranging applications. The purpose is to detect object contours and track their motion in a video. Issues of concern are to be able to map objects correctly between two frames, and to be able to track through occlusion. This thesis discusses a novel framework for the purpose of object tracking which is inspired from image registration and segmentation models.
Occlusion Reasoning For Multiple Object Visual Tracking
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Author : Zheng Wu
language : en
Publisher:
Release Date : 2013
Occlusion Reasoning For Multiple Object Visual Tracking written by Zheng Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Abstract: Occlusion reasoning for visual object tracking in uncontrolled environments is a challenging problem. It becomes significantly more difficult when dense groups of indistinguishable objects are present in the scene that cause frequent inter-object interactions and occlusions. We present several practical solutions that tackle the inter-object occlusions for video surveillance applications. In particular, this thesis proposes three methods. First, we propose "reconstruction-tracking," an online multi-camera spatial-temporal data association method for tracking large groups of objects imaged with low resolution. As a variant of the well-known Multiple-Hypothesis-Tracker, our approach localizes the positions of objects in 3D space with possibly occluded observations from multiple camera views and performs temporal data association in 3D. Second, we develop "track linking," a class of offline batch processing algorithms for long-term occlusions, where the decision has to be made based on the observations from the entire tracking sequence. We construct a graph representation to characterize occlusion events and propose an efficient graph-based/combinatorial algorithm to resolve occlusions. Third, we propose a novel Bayesian framework where detection and data association are combined into a single module and solved jointly. Almost all traditional tracking systems address the detection and data association tasks separately in sequential order. Such a design implies that the output of the detector has to be reliable in order to make the data association work. Our framework takes advantage of the often complementary nature of the two subproblems, which not only avoids the error propagation issue from which traditional "detection-tracking approaches" suffer but also eschews common heuristics such as "non-maximum suppression" of hypotheses by modeling the likelihood of the entire image. The thesis describes a substantial number of experiments, involving challenging, notably distinct simulated and real data, including infrared and visible-light data sets recorded ourselves or taken from data sets publicly available. In these videos, the number of objects ranges from a dozen to a hundred per frame in both monocular and multiple views. The experiments demonstrate that our approaches achieve results comparable to those of state-of-the-art approaches.
Data Association For Multi Object Visual Tracking
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Author : Margrit Betke
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Data Association For Multi Object Visual Tracking written by Margrit Betke and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
In the human quest for scientific knowledge, empirical evidence is collected by visual perception. Tracking with computer vision takes on the important role to reveal complex patterns of motion that exist in the world we live in. Multi-object tracking algorithms provide new information on how groups and individual group members move through three-dimensional space. They enable us to study in depth the relationships between individuals in moving groups. These may be interactions of pedestrians on a crowded sidewalk, living cells under a microscope, or bats emerging in large numbers from a cave. Being able to track pedestrians is important for urban planning; analysis of cell interactions supports research on biomaterial design; and the study of bat and bird flight can guide the engineering of aircraft. We were inspired by this multitude of applications to consider the crucial component needed to advance a single-object tracking system to a multi-object tracking system—data association. Data association in the most general sense is the process of matching information about newly observed objects with information that was previously observed about them. This information may be about their identities, positions, or trajectories. Algorithms for data association search for matches that optimize certain match criteria and are subject to physical conditions. They can therefore be formulated as solving a "constrained optimization problem"—the problem of optimizing an objective function of some variables in the presence of constraints on these variables. As such, data association methods have a strong mathematical grounding and are valuable general tools for computer vision researchers. This book serves as a tutorial on data association methods, intended for both students and experts in computer vision. We describe the basic research problems, review the current state of the art, and present some recently developed approaches. The book covers multi-object tracking in two and three dimensions. We consider two imaging scenarios involving either single cameras or multiple cameras with overlapping fields of view, and requiring across-time and across-view data association methods. In addition to methods that match new measurements to already established tracks, we describe methods that match trajectory segments, also called tracklets. The book presents a principled application of data association to solve two interesting tasks: first, analyzing the movements of groups of free-flying animals and second, reconstructing the movements of groups of pedestrians. We conclude by discussing exciting directions for future research.
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.
Computer Vision Eccv 2012
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Author : Andrew Fitzgibbon
language : en
Publisher: Springer
Release Date : 2012-09-26
Computer Vision Eccv 2012 written by Andrew Fitzgibbon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-26 with Computers categories.
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Taking Mobile Multi Object Tracking To The Next Level
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Author : Dennis Mitzel
language : en
Publisher:
Release Date : 2014
Taking Mobile Multi Object Tracking To The Next Level written by Dennis Mitzel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Automatic tracking categories.
Recent years have seen considerable progress in automotive safety and autonomous navigation applications, fueled by the remarkable advance of individual Computer Vision components, such as object detection, tracking, stereo and visual odometry. The goal in such applications is to automatically infer semantic understanding from the environment, observed from a moving vehicle equipped with a camera system. The pedestrian detection and tracking components constitute an actively researched part in scene understanding, important for safe navigation, path planning, and collision avoidance. Classical tracking-by-detection approaches require a robust object detector that needs to be executed in every frame. However, the detector is typically the most computationally expensive component, especially if more than one object class needs to be detected. A first goal of this thesis was to develop a vision system based on stereo camera input that is able to detect and track multiple pedestrians in real-time. To this end, we propose a hybrid tracking system that combines a computationally cheap low-level tracker with a more complex high-level tracker. The low-level trackers are either based on level-set segmentation or stereo range data together with a point registration algorithm and are employed in order to follow individual pedestrians over time, starting from an initial object detection. In order to cope with drift and to bridge occlusions that cannot be resolved by low-level trackers, the resulting tracklet outputs are fed to a high-level multihypothesis tracker, which performs longer-term data association. With this integration we obtain a real-time tracking framework by reducing object detector applications to fewer frames or even to few small image regions when stereo data is available. Reduction of expensive detector evaluations is especially relevant for the deployment on mobile platforms, where real-time performance is crucial and computational resources are notoriously
Pattern Recognition
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Author : Fred A. Hamprecht
language : en
Publisher: Springer
Release Date : 2007-09-22
Pattern Recognition written by Fred A. Hamprecht and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-22 with Computers categories.
This book constitutes the refereed proceedings of the 29th Symposium of the German Association for Pattern Recognition, DAGM 2007. It covers image filtering, restoration and segmentation, shape analysis and representation, categorization and detection, computer vision and image retrieval, machine learning and statistical data analysis, biomedical data analysis, motion analysis and tracking, stereo and structure from motion, as well as 3D view registration and surface modeling.
Feature Based Probabilistic Data Association For Video Based Multi Object Tracking
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Author : Grinberg, Michael
language : en
Publisher: KIT Scientific Publishing
Release Date : 2018-08-10
Feature Based Probabilistic Data Association For Video Based Multi Object Tracking written by Grinberg, Michael and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-10 with categories.
Moving Objects Detection Using Machine Learning
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Author : Navneet Ghedia
language : en
Publisher: Springer Nature
Release Date : 2022-01-01
Moving Objects Detection Using Machine Learning written by Navneet Ghedia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Technology & Engineering categories.
This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.
Random Finite Sets For Robot Mapping Slam
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Author : John Stephen Mullane
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-19
Random Finite Sets For Robot Mapping Slam written by John Stephen Mullane 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 2011-05-19 with Technology & Engineering categories.
The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.