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single camera view</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="5692" href="https://surrey.academia.edu/RichardBowden">Richard Bowden</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the British Machine …, 1998</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":"Reconstructing 3d pose and motion from a single camera view","attachmentId":2966015,"attachmentType":"pdf","work_url":"https://www.academia.edu/575714/Reconstructing_3d_pose_and_motion_from_a_single_camera_view","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/575714/Reconstructing_3d_pose_and_motion_from_a_single_camera_view"><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="18155894" 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/18155894/Temporal_motion_models_for_monocular_and_multiview_3D_human_body_tracking">Temporal motion models for monocular and multiview 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="38120728" href="https://independent.academia.edu/RaquelUrtasun">Raquel Urtasun</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computer Vision and Image Understanding, 2006</p><p class="ds-related-work--abstract ds2-5-body-sm">We explore an approach to 3D people tracking with learned motion models and deterministic optimization. The tracking problem is formulated as the minimization of a differentiable criterion whose differential structure is rich enough for optimization to be accomplished via hill-climbing. This avoids the computational expense of Monte Carlo methods, while yielding good results under challenging conditions. To demonstrate the generality of the approach we show that we can learn and track cyclic motions such as walking and running, as well as acyclic motions such as a golf swing. We also show results from both monocular and multi-camera tracking. Finally, we provide results with a motion model learned from multiple activities, and show how this models might be used for recognition.</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":"Temporal motion models for monocular and multiview 3D human body tracking","attachmentId":39905105,"attachmentType":"pdf","work_url":"https://www.academia.edu/18155894/Temporal_motion_models_for_monocular_and_multiview_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/18155894/Temporal_motion_models_for_monocular_and_multiview_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="2" data-entity-id="7944438" 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/7944438/Modeling_Human_Bodies_from_Video_Sequences">Modeling Human Bodies from Video Sequences</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="15117113" href="https://epfl.academia.edu/RalfPl%C3%A4nkers">Ralf Plänkers</a></div><p class="ds-related-work--metadata ds2-5-body-xs">1999</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we show that, given video sequences of a moving person acquired with a multi-camera system, we can track joint locations during the movement and recover shape information. We outline techniques for fitting a simplified model to the noisy 3-D data extracted from the images and a new tracking process based on least squares matching is presented. The recovered shape and motion parameters can be used to either reconstruct the original sequence or to allow other animation models to mimic the subject's actions. Our utlimate goal is to automate the process of building complete and realistic animation models of humans, given a set of video sequences.</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":"Modeling Human Bodies from Video Sequences","attachmentId":48280565,"attachmentType":"pdf","work_url":"https://www.academia.edu/7944438/Modeling_Human_Bodies_from_Video_Sequences","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/7944438/Modeling_Human_Bodies_from_Video_Sequences"><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="13350173" 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/13350173/Stochastic_tracking_of_3D_human_figures_using_2D_image_motion">Stochastic tracking of 3D human figures using 2D image motion</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="32587331" href="https://espol.academia.edu/MichaelBlack">Michael Black</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2000</p><p class="ds-related-work--abstract ds2-5-body-sm">A probabilistic method for tracking 3D articulated human figures in monocular image sequences is presented. Within a Bayesian framework, we define a generative model of image appearance, a robust likelihood function based on image graylevel differences, and a prior probability distribution over pose and joint angles that models how humans move. The posterior probability distribution over model parameters is represented using a discrete set of samples and is propagated over time using particle filtering. The approach extends previous work on parameterized optical flow estimation to exploit a complex 3D articulated motion model. It also extends previous work on human motion tracking by including a perspective camera model, by modeling limb self occlusion, and by recovering 3D motion from a monocular sequence. The explicit posterior probability distribution represents ambiguities due to image matching, model singularities, and perspective projection. The method relies only on a frame-to-frame assumption of brightness constancy and hence is able to track people under changing viewpoints, in grayscale image sequences, and with complex unknown backgrounds.</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":"Stochastic tracking of 3D human figures using 2D image motion","attachmentId":45440181,"attachmentType":"pdf","work_url":"https://www.academia.edu/13350173/Stochastic_tracking_of_3D_human_figures_using_2D_image_motion","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/13350173/Stochastic_tracking_of_3D_human_figures_using_2D_image_motion"><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="12775177" 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/12775177/_title_Modeling_human_bodies_from_video_sequences_title_"><title>Modeling human bodies from video sequences</title></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="31831284" href="https://ethz.academia.edu/ArminGruen">Armin Gruen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Videometrics VI, 1998</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we show that, given video sequences of a moving person acquired with a multi-camera system, we can track joint locations during the movement and recover shape information. We outline techniques for fitting a simplified model to the noisy 3-D data extracted from the images and a new tracking process based on least squares matching is presented. The recovered shape and motion parameters can be used to either reconstruct the original sequence or to allow other animation models to mimic the subject's actions. Our utlimate goal is to automate the process of building complete and realistic animation models of humans, given a set of video sequences.</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":"\u003ctitle\u003eModeling human bodies from video sequences\u003c/title\u003e","attachmentId":45943639,"attachmentType":"pdf","work_url":"https://www.academia.edu/12775177/_title_Modeling_human_bodies_from_video_sequences_title_","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/12775177/_title_Modeling_human_bodies_from_video_sequences_title_"><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="7944433" 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/7944433/HUMAN_BODY_MODELING_AND_MOTION_ANALYSIS_FROM_VIDEO_SEQUENCES">HUMAN BODY MODELING AND MOTION ANALYSIS FROM VIDEO SEQUENCES</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="15117113" href="https://epfl.academia.edu/RalfPl%C3%A4nkers">Ralf Plänkers</a></div><p class="ds-related-work--metadata ds2-5-body-xs">1998</p><p class="ds-related-work--abstract ds2-5-body-sm">We present a comprehensive concept to fit animation models to a variety of different data derived from multi-image video sequences. Our goal is to record dynamically the body surface of a human in motion and to model it for animation purposes. This includes setting up and calibrating a system of three CCD-cameras, extracting image silhouettes, tracking individual key body points in 3-D, and generating surface data by stereo or multi-image matching. All these observations are brought together under a joint least squares estimation system, from which the body model parameters are derived. This represents a first report concerning our concept. The presented data stems from individual tests and is highly incomplete. However, these results support strongly the chosen concept and will lead to further developments and refinements.</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":"HUMAN BODY MODELING AND MOTION ANALYSIS FROM VIDEO SEQUENCES","attachmentId":48280612,"attachmentType":"pdf","work_url":"https://www.academia.edu/7944433/HUMAN_BODY_MODELING_AND_MOTION_ANALYSIS_FROM_VIDEO_SEQUENCES","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/7944433/HUMAN_BODY_MODELING_AND_MOTION_ANALYSIS_FROM_VIDEO_SEQUENCES"><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="7944426" 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/7944426/Tracking_and_Modeling_People_in_Video_Sequences">Tracking and Modeling People in Video Sequences</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="15117113" href="https://epfl.academia.edu/RalfPl%C3%A4nkers">Ralf Plänkers</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computer Vision and Image Understanding, 2001</p><p class="ds-related-work--abstract ds2-5-body-sm">Tracking and modeling people from video sequences has become an increasingly important research topic, with applications including animation, surveillance and sports medicine. In this paper, we propose a model based 3-D approach to recovering both body shape and motion. It takes advantage of a sophisticated animation model to achieve both robustness and realism. Stereo sequences of people in motion serve as input to our system. From these, we extract a 2 1 2 -D description of the scene and, optionally, silhouette edges. We propose an integrated framework to fit the model and to track the person's motion. The environment does not have to be engineered. We recover not only the motion but also a full animation model closely resembling the subject. We present results of our system on real sequences and we show the generic model adjusting to the person and following various kinds of motion.</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 and Modeling People in Video Sequences","attachmentId":48280558,"attachmentType":"pdf","work_url":"https://www.academia.edu/7944426/Tracking_and_Modeling_People_in_Video_Sequences","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/7944426/Tracking_and_Modeling_People_in_Video_Sequences"><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="49722683" 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/49722683/Human_body_modelling_and_tracking_using_volumetric_representation_Selected_recent_studies_and_possibilities_for_extensions">Human body modelling and tracking using volumetric representation: Selected recent studies and possibilities for extensions</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="162436479" href="https://cnn.academia.edu/MohanTrivedi">Mohan Trivedi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2008 Second ACM/IEEE International Conference on Distributed Smart Cameras, 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">Articulated human body modeling and tracking from vision data is an attractive research area with many potential applications. There has been a tremendous amount of related research works in this area. Therefore, having a comprehensive insight into high quality existing works and awareness of the research frontier in the area is essential for follow-up research studies. With that objective, this paper provides a review of the subarea of model based methods for human body modeling and tracking using volumetric (voxel) data. We will focus on analyzing and comparing some recent techniques, especially which are in the past two years, in order to highlight trends in the domain as well as to point out limitations of the current state of the art. Based on this analysis, we will discuss our idea of combining Laplacian Eigenspace (LE) based voxel segmentation [20] and Kinematically Constrained Gaussian Mixture Model (KC-GMM) method [3] to have a more powerful human body pose estimation system as well as discuss other possibilities for future work.</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":"Human body modelling and tracking using volumetric representation: Selected recent studies and possibilities for extensions","attachmentId":67986098,"attachmentType":"pdf","work_url":"https://www.academia.edu/49722683/Human_body_modelling_and_tracking_using_volumetric_representation_Selected_recent_studies_and_possibilities_for_extensions","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/49722683/Human_body_modelling_and_tracking_using_volumetric_representation_Selected_recent_studies_and_possibilities_for_extensions"><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="25120370" 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/25120370/Tracking_of_the_Articulated_Upper_Body_on_Multi_View_Stereo_Image_Sequences">Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences</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="48335347" href="https://independent.academia.edu/KNickel2">Kai Nickel</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2000</p><p class="ds-related-work--abstract ds2-5-body-sm">We propose a novel method for tracking an articulated model in a 3D-point cloud. The tracking problem is formulated as the registration of two point sets, one of them parameterised by the model's state vector and the other acquired from a 3D-sensor system. Finding the correct parameter vector is posed as a linear estimation problem, which is solved by means of a scaled unscented Kalman filter. Our method draws on concepts from the widely used iterative closest point registration algorithm (ICP), basing the measurement model on point correspondences established between the synthesised model point cloud and the measured 3D-data. We apply the algorithm to kinematically track a model of the human upper body on a point cloud obtained through stereo image processing from one or more stereo cameras. We determine torso position and orientation as well as joint angles of shoulders and elbows. The algorithm has been successfully tested on thousands of frames of real image data. Challenging sequences of several minutes length where tracked correctly. Complete processing time remains below one second per frame.</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 of the Articulated Upper Body on Multi-View Stereo Image Sequences","attachmentId":45440982,"attachmentType":"pdf","work_url":"https://www.academia.edu/25120370/Tracking_of_the_Articulated_Upper_Body_on_Multi_View_Stereo_Image_Sequences","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/25120370/Tracking_of_the_Articulated_Upper_Body_on_Multi_View_Stereo_Image_Sequences"><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="32236829" 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/32236829/Model_based_human_gait_tracking_3D_reconstruction_and_recognition_in_uncalibrated_monocular_video">Model-based human gait tracking, 3D reconstruction and recognition in uncalibrated monocular video</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="62482908" href="https://independent.academia.edu/FarzadZargari">Farzad Zargari</a></div><p class="ds-related-work--metadata ds2-5-body-xs">The Imaging Science Journal, 2012</p><p class="ds-related-work--abstract ds2-5-body-sm">Automatic analysis of human motion includes initialisation, tracking, pose recovery and activity recognition. In this paper, a computing framework is developed to automatically analyse human motions through uncalibrated monocular video sequences. A model-based kinematics approach is proposed for human gait tracking. Based on the tracking results, 3D human poses and gait features are recovered and extracted. The recognition performance is evaluated by using different classifiers. The proposed method is advantageous in its capability of recognising human subjects walking non-parallel to the image plane.</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":"Model-based human gait tracking, 3D reconstruction and recognition in uncalibrated monocular video","attachmentId":52460672,"attachmentType":"pdf","work_url":"https://www.academia.edu/32236829/Model_based_human_gait_tracking_3D_reconstruction_and_recognition_in_uncalibrated_monocular_video","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/32236829/Model_based_human_gait_tracking_3D_reconstruction_and_recognition_in_uncalibrated_monocular_video"><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":2966014,"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":2966014,"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_2966014" 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. 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