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This issue has been widely studied in the literature on RGB images" /> <meta property="article:author" content="https://gadz.academia.edu/FakhreddineAbabsa" /> <meta name="description" content="The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images" /> <title>(PDF) 3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter | Fakhreddine Ababsa - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '39314d9bcf4522f48eeb027cf31da0a13496d2ce'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1733911312000); window.Aedu.timeDifference = new Date().getTime() - 1733911312000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Squa...","author":[{"@context":"https://schema.org","@type":"Person","name":"Fakhreddine Ababsa"}],"contributor":[],"dateCreated":"2023-12-16","datePublished":"2020-01-01","headline":"3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter","image":"https://attachments.academia-assets.com/109098705/thumbnails/1.jpg","inLanguage":"en","keywords":["Computer Science","Artificial Intelligence","Analytical Chemistry","Computer Vision","Medicine","Sensors","Human Tracking","Particle Filter","Electrical And Electronic Engineering","Silhouette","Omnidirectional Camera","Mean Squared Error"],"publication":"Sensors","publisher":{"@context":"https://schema.org","@type":"Organization","name":"MDPI 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window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":111609803,"created_at":"2023-12-16T23:55:55.811-08:00","from_world_paper_id":245980100,"updated_at":"2023-12-17T00:23:03.998-08:00","_data":{"abstract":"The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Squa...","publisher":"MDPI AG","publication_date":"2020,,","publication_name":"Sensors"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [263837202]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":109098705,"attachmentType":"pdf"}"><img alt="First page of “3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/109098705/mini_magick20231217-1-nixufq.png?1702800210" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="263837202" href="https://gadz.academia.edu/FakhreddineAbabsa"><img alt="Profile image of Fakhreddine Ababsa" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/263837202/115339392/104625007/s65_fakhreddine.ababsa.png" />Fakhreddine Ababsa</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2020, Sensors</p></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Squa...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":109098705,"attachmentType":"pdf","workUrl":"https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":109098705,"attachmentType":"pdf","workUrl":"https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="109098705" data-landing_url="https://www.academia.edu/111609803/3D_Human_Pose_Estimation_with_a_Catadioptric_Sensor_in_Unconstrained_Environments_Using_an_Annealed_Particle_Filter" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="111609806" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/111609806/3D_Human_Tracking_with_Catadioptric_Omnidirectional_Camera">3D Human Tracking with Catadioptric Omnidirectional Camera</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="263837202" href="https://gadz.academia.edu/FakhreddineAbabsa">Fakhreddine Ababsa</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 2019 on International Conference on Multimedia Retrieval, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper deals with the problem of 3D human tracking in catadioptric images using particle-filtering framework. While traditional perspective images are well exploited, only a few methods have been developed for catadioptric vision, for the human detection or tracking problems. We propose to extend the 3D pose estimation in the case of perspective cameras to catadioptric sensors. In this paper, we develop an original likelihood functions based, on the one hand, on the geodetic distance in the spherical space SO 3 and, on the other hand, on the mapping between the human silhouette in the images and the projected 3D model. These likelihood functions combined with a particle filter, whose propagation model is adapted to the spherical space, allow accurate 3D human tracking in omnidirectional images. Both visual and quantitative analysis of the experimental results demonstrate the effectiveness of our approach</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"3D Human Tracking with Catadioptric Omnidirectional Camera","attachmentId":109098770,"attachmentType":"pdf","work_url":"https://www.academia.edu/111609806/3D_Human_Tracking_with_Catadioptric_Omnidirectional_Camera","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/111609806/3D_Human_Tracking_with_Catadioptric_Omnidirectional_Camera"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="9437635" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/9437635/3D_Tracking_by_Catadioptric_Vision_Based_on_Particle_Filters">3D Tracking by Catadioptric Vision Based on Particle Filters</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="21975350" href="https://tecnico.academia.edu/AlexandreBernardino">Alexandre Bernardino</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2007</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a robust tracking system for autonomous robots equipped with omnidirectional cameras. The proposed method uses a 3D shape and color-based object model. This allows to tackle difficulties that arise when the tracked object is placed above the ground plane floor. Tracking under these conditions has two major difficulties: first, observation with omnidirectional sensors largely deforms the target’s shape; second, the object of interest embedded in a dynamic scenario may suffer from occlusion, overlap and ambiguities. To surmount these difficulties, we use a 3D particle filter to represent the target’s state space: position and velocity with respect to the robot. To compute the likelihood of each particle the following features are taken into account: i) image color; ii) mismatch between target’s color and background color. We test the accuracy of the algorithm in a RoboCup Middle Size League scenario, both with static and moving targets.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"3D Tracking by Catadioptric Vision Based on Particle Filters","attachmentId":47780678,"attachmentType":"pdf","work_url":"https://www.academia.edu/9437635/3D_Tracking_by_Catadioptric_Vision_Based_on_Particle_Filters","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/9437635/3D_Tracking_by_Catadioptric_Vision_Based_on_Particle_Filters"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="87470540" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/87470540/Rao_Blackwellized_Particle_Filter_for_Human_Appearance_and_Position_Tracking">Rao-Blackwellized Particle Filter for Human Appearance and Position Tracking</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32829794" href="https://independent.academia.edu/CarlosOrrite">Carlos Orrite</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lecture Notes in Computer Science</p><p class="ds-related-work--abstract ds2-5-body-sm">In human motion analysis, the joint estimation of appearance, body pose and location parameters is not always tractable due to its huge computational cost. In this paper, we propose a Rao-Blackwellized Particle Filter for addressing the problem of human pose estimation and tracking. The advantage of the proposed approach is that Rao-Blackwellization allows the state variables to be splitted into two sets, being one of them analytically calculated from the posterior probability of the remaining ones. This procedure reduces the dimensionality of the Particle Filter, thus requiring fewer particles to achieve a similar tracking performance. In this manner, location and size over the image are obtained stochastically using colour and motion clues, whereas body pose is solved analytically applying learned human Point Distribution Models.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Rao-Blackwellized Particle Filter for Human Appearance and Position Tracking","attachmentId":91669670,"attachmentType":"pdf","work_url":"https://www.academia.edu/87470540/Rao_Blackwellized_Particle_Filter_for_Human_Appearance_and_Position_Tracking","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87470540/Rao_Blackwellized_Particle_Filter_for_Human_Appearance_and_Position_Tracking"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="10827050" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/10827050/A_Study_on_Smoothing_for_Particle_Filtered_3D_Human_Body_Tracking">A Study on Smoothing for Particle-Filtered 3D Human Body Tracking</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="26321104" href="https://curtin.academia.edu/GeoffWest">Geoff West</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Vision, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">Stochastic models have become the dominant means of approaching the problem of articulated 3D human body tracking, where approximate inference is employed to tractably estimate the high-dimensional (∼30D) posture space. Of these approximate inference techniques, particle filtering is the most commonly used approach. However filtering only takes into account past observations-almost no body tracking research employs smoothing to improve the filtered inference estimate, despite the fact that smoothing considers both past and future evidence and so should be more accurate. In an effort to objectively determine the worth of existing smoothing algorithms when applied to human body tracking, this paper investigates three approximate smoothed-inference techniques: particle-filtered backwards smoothing, variational approximation and Gibbs sampling. Results are quantitatively evaluated on both the HU-MANEVA dataset as well as a scene containing occluding clutter. Surprisingly, it is found that existing smoothing techniques are unable to provide much improvement on the filtered estimate, and possible reasons as to why are explored and discussed.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Study on Smoothing for Particle-Filtered 3D Human Body Tracking","attachmentId":47094647,"attachmentType":"pdf","work_url":"https://www.academia.edu/10827050/A_Study_on_Smoothing_for_Particle_Filtered_3D_Human_Body_Tracking","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/10827050/A_Study_on_Smoothing_for_Particle_Filtered_3D_Human_Body_Tracking"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="18184796" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/18184796/On_the_use_of_perspective_catadioptric_sensors_for_3D_model_based_tracking_with_particle_filters">On the use of perspective catadioptric sensors for 3D model-based tracking with particle filters</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="21975350" href="https://tecnico.academia.edu/AlexandreBernardino">Alexandre Bernardino</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007</p><p class="ds-related-work--abstract ds2-5-body-sm">We present a model-based 3D tracking system, using wide angle perspective catadioptric sensors. These sensors acquire 360 o views of the environment and the projection from 3D world points to the image plane is approximated by a perspective model. This is a major advantage in structured environments because straight lines on specific surfaces are not deformed by the sensor, allowing the application of standard computer vision algorithms. Objects off the surface are distorted according to a complex projection model, but can be approximated by a simple wide angle perspective mapping. This is exploited here to develop a robust tracking system for autonomous robots using a 3D shape and color-based object model. The use of particle filters allows tracking to be done with 3D realistic motion models and tackling object occlusion, overlap and ambiguities. We show that the use of the perspective model is advantageous over more standard catadioptric projection models, since it renders a very good approximation to the true model, being simpler and more efficient to use, in particular with 3D particle filtering methods.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"On the use of perspective catadioptric sensors for 3D model-based tracking with particle filters","attachmentId":39921275,"attachmentType":"pdf","work_url":"https://www.academia.edu/18184796/On_the_use_of_perspective_catadioptric_sensors_for_3D_model_based_tracking_with_particle_filters","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/18184796/On_the_use_of_perspective_catadioptric_sensors_for_3D_model_based_tracking_with_particle_filters"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="5892333" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/5892333/Efficient_posture_estimation_using_the_particle_filter">Efficient posture estimation using the particle filter</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="8725126" href="https://hiroshima-cu.academia.edu/NobuoSuematsu">Nobuo Suematsu</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Congress Series, 2006</p><p class="ds-related-work--abstract ds2-5-body-sm">We address the following problem: estimate the posture vector sequence from a monocular image feature vector sequence. Its solution can be used to realize visual interfaces between humans and intelligent machines. We take the state space approach, that is, the posture estimation is formulated as the state estimation. We propose a cyclic motion model (CMM) whose state variable is the phase of a motion, and estimate the postures using a particle filter. In addition, we extend CMM so that the posture estimation is possible from image sequences with camera angle change. D 2006 Published by Elsevier B.V.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Efficient posture estimation using the particle filter","attachmentId":49097905,"attachmentType":"pdf","work_url":"https://www.academia.edu/5892333/Efficient_posture_estimation_using_the_particle_filter","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/5892333/Efficient_posture_estimation_using_the_particle_filter"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="87470567" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/87470567/1_Tracking_Human_Position_and_Lower_Body_Parts_Using_Kalman_and_Particle_Filters_Constrained_by">1 Tracking Human Position and Lower Body Parts Using Kalman and Particle Filters Constrained by</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32829794" href="https://independent.academia.edu/CarlosOrrite">Carlos Orrite</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2012</p><p class="ds-related-work--abstract ds2-5-body-sm">Abstract—In this paper, a novel framework for visual tracking of human body parts is introduced. The presented approach demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera using a limb tracking system based on a 2D articulated model and a double tracking strategy. Its key contribution is that the 2D model is only constrained by biomechanical knowledge about human bipedal motion, instead of relying on constraints linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on a set of indoor and outdoor sequences demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented. Index Terms—human pose, particle filter, biomechanics, 2D articulated model, bipedal motion, video surveillance.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"1 Tracking Human Position and Lower Body Parts Using Kalman and Particle Filters Constrained by","attachmentId":91669684,"attachmentType":"pdf","work_url":"https://www.academia.edu/87470567/1_Tracking_Human_Position_and_Lower_Body_Parts_Using_Kalman_and_Particle_Filters_Constrained_by","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87470567/1_Tracking_Human_Position_and_Lower_Body_Parts_Using_Kalman_and_Particle_Filters_Constrained_by"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="191864" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/191864/Tracking_Human_Body_Parts_Using_Particle_Filters_Constrained_by_Human_Biomechanics">Tracking Human Body Parts Using Particle Filters Constrained by Human Biomechanics</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33952498" href="https://qub.academia.edu/Jes%C3%BAsMart%C3%ADnezdelRinc%C3%B3n">Jesús Martínez del Rincón</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="67407" href="https://kingston.academia.edu/JeanChristopheNebel">Jean-Christophe Nebel</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, a novel framework for visual tracking of human body parts is introduced. The presented approach demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera using a limb tracking system based on a 2D articulated model. It is constrained only by biomechanical knowledge about human bipedal motion, instead on relying on constraints linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on HumanEva I & II datasets demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Tracking Human Body Parts Using Particle Filters Constrained by Human Biomechanics","attachmentId":394806,"attachmentType":"pdf","work_url":"https://www.academia.edu/191864/Tracking_Human_Body_Parts_Using_Particle_Filters_Constrained_by_Human_Biomechanics","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/191864/Tracking_Human_Body_Parts_Using_Particle_Filters_Constrained_by_Human_Biomechanics"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="20098895" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/20098895/APPEARANCE_BASED_PERSON_TRACKING_AND_3D_POSE_ESTIMATION_OF_UPPER_BODY_AND_HEAD">APPEARANCE-BASED PERSON TRACKING AND 3D POSE ESTIMATION OF UPPER-BODY AND HEAD</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="41124901" href="https://independent.academia.edu/CWeinrich">Christoph Weinrich</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In the field of human-robot interaction (HRI), recognition of humans in a robot's surroundings is a crucial task. Besides the localization, the estimation of a person's 3D pose based on monocular camera images is a challenging problem on a mobile platform. For this purpose, an appearancebased approach, using a 3D model of the human upper body, has been developed end experimentally investigated. For a real time tracking, the state of the person is estimated by a particle filter tracker, which uses different observation models for evaluating pose hypotheses. The 6D body pose is modeled by 4 parameters for the torso position and orientation as well as 2 for the head pan and tilt. In order to achieve real time operation, a smooth fit value function simplifies the particle filter's convergence. Futhermore, a sparse feature based model eliminates the need for computationally expensive geometric transformations of the image, as required by conventional Active Appearance Models (AAM). The initialization problem of the pose tracker is overcome by integrating a Histograms of Oriented Gradients (HOG) detector.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"APPEARANCE-BASED PERSON TRACKING AND 3D POSE ESTIMATION OF UPPER-BODY AND HEAD","attachmentId":41173209,"attachmentType":"pdf","work_url":"https://www.academia.edu/20098895/APPEARANCE_BASED_PERSON_TRACKING_AND_3D_POSE_ESTIMATION_OF_UPPER_BODY_AND_HEAD","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/20098895/APPEARANCE_BASED_PERSON_TRACKING_AND_3D_POSE_ESTIMATION_OF_UPPER_BODY_AND_HEAD"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="349177" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/349177/Tracking_Human_Position_and_Body_Parts_Using_Kalman_and_Particle_Filters_Constrained_by_Human_Biomechanics">Tracking Human Position and Body Parts Using Kalman and Particle Filters Constrained by Human Biomechanics</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="67407" href="https://kingston.academia.edu/JeanChristopheNebel">Jean-Christophe Nebel</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32829794" href="https://independent.academia.edu/CarlosOrrite">Carlos Orrite</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, a novel framework for visual tracking of human body parts is introduced. The presented approach demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera using a limb tracking system based on a 2D articulated model and a double tracking strategy. Its key contribution is that the 2D model is only constrained by biomechanical knowledge about human bipedal motion, instead of relying on constraints linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on a set of indoor and outdoor sequences demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Tracking Human Position and Body Parts Using Kalman and Particle Filters Constrained by Human Biomechanics","attachmentId":6978395,"attachmentType":"pdf","work_url":"https://www.academia.edu/349177/Tracking_Human_Position_and_Body_Parts_Using_Kalman_and_Particle_Filters_Constrained_by_Human_Biomechanics","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/349177/Tracking_Human_Position_and_Body_Parts_Using_Kalman_and_Particle_Filters_Constrained_by_Human_Biomechanics"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":109098705,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":109098705,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_109098705" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="68531206" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/68531206/3D_Tracking_Using_Multi_view_Based_Particle_Filters">3D Tracking Using Multi-view Based Particle Filters</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="40435995" href="https://independent.academia.edu/LuisSalgado33">Luis Salgado</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline 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href="https://www.academia.edu/49183615/A_quantitative_evaluation_of_video_based_3D_person_tracking">A quantitative evaluation of video-based 3D person tracking</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="166455041" href="https://independent.academia.edu/AlexandruBalan28">Alexandru Balan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2005</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A quantitative evaluation of video-based 3D person tracking","attachmentId":67567956,"attachmentType":"pdf","work_url":"https://www.academia.edu/49183615/A_quantitative_evaluation_of_video_based_3D_person_tracking","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span 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href="https://surrey.academia.edu/RichardBowden">Richard Bowden</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computer Vision and Image …, 2006</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Viewpoint invariant exemplar-based 3D human tracking","attachmentId":2966066,"attachmentType":"pdf","work_url":"https://www.academia.edu/575721/Viewpoint_invariant_exemplar_based_3D_human_tracking","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/575721/Viewpoint_invariant_exemplar_based_3D_human_tracking"><span class="ds2-5-text-link__content">View PDF</span><span 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