The topic of our research is to explain how we can use new technology to transform traditional contents into popular media format while preserving the originality of the art and culture . We found a process to integrate art , culture , woodcut print and folk dance with technology and animation , by using motion capture , 3d animation and photo retouch technology . Motion capture remains a popular and widely-used method for animating virtual characters . However , all practical applications of motion capture rely on motion editing techniques to increase the reusability and flexibility of captured motions . Because humans are proficient in detecting and interpreting subtle details in human motion , understanding the perceptual consequences of motion editing is essential . The fiber orientations were automatically computed , while the muscle line of action was determined using approaches based on anatomical landmarks and on centroids of image segmentation . Three healthy male volunteers were recruited for MRI scanning and motion capture acquisition . This work evaluates the effect of subject-specific pennation angle as musculoskeletal parameter in the lower limb , focusing on the quadriceps group . Motion capture analysis of sit-to-stand task with assistive device is hard to achieve due to obstruction on reflective makers . Previously developed robotic system , Smart Mobile Walker , is used as an assistive device to perform motion capture analysis in sit-to-stand task . All lower limb markers except hip markers are invisible through whole session . Moreover , it is low cost , portable and easy to use . As a conclusion , the current study and its findings are found useful and has contributed to enhance significant knowledge pertaining to motion capture-analysis , drop jump movement and HD VideoCam – Kinovea integration . Experiments show that our method not only has a good performance in behavioral segmentation for motion capture data , but also finds cycles of motion and the motion clips of the same behaviors from long original motion sequence . Keywords-motion analysis ; behavioral segmentation ; clustering ; motion capture data ; cycle In the preliminary works the flexion-extension of the elbow joint only is considered . Experimental set-up includes EMGs acquisition and motion capture data . Movements are chosen to limit cocontraction of antagonist muscles . We present an animation interface designed to conveniently control the motion capture process . The transition of performer 's hand gestures tracked by a dataglove is recognized for software remote control . Reconstruction is a key step of the motion capture process . The quality of motion data first results from the quality of raw data . Vision-based object tracking has lots of applications in robotics , like surveillance , navigation , motion capturing , and so on . However , the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms . Simulated annealing , on the other hand , is a global optimization method derived from statistical mechanics . A recent heuristic approach to fuse these two techniques for motion capturing has become known as annealed particle filter . In order to analyze these techniques , we rigorously derive in this paper two algorithms with annealing properties based on the mathematical theory of interacting particle systems . The Acrobot model , its physical structure , the degree of freedom and the technical requirements of International Humanoid-robot Olympic Games(IHOG ) were analyzed for the robot on a horizontal bar . The key steps of this dynamic motion strategy are motion data capture through the video of human 's motion on the horizontal bar , the key feature points ' analysis of the humanoid robot model and the joint angle 's kinematic data match in the basic movements . After making the appropriate adjustments due to the kinematic constraints , an interpolation method was used to generate the motion trajectory of all the joints . The International Society of Biomechanics ( ISB ) has recommended a standardization of the definition of the joint coordinate system ( JCS ) and use of a sequential rotation to describe human shoulder joint rotation . Markers attached to the surface of the body may move during the process of motion data capture , resulting in an offset from their initial location . This leads to a change of the JCS and therefore affects the calculated shoulder joint angles . The methodology applies background extraction using the Gaussian Mixture Model ( GMM ) , a scale reduction based on the Wavelet Transform ( WT ) and feature extraction by Principal Component Analysis ( PCA ) . We propose four new schemas for motion information capture : the Silhouette-Gray-Wavelet model ( SGW ) captures motion based on grey level variations ; the Silhouette-Binary-Wavelet model ( SBW ) captures motion based on binary information ; the Silhouette – Edge-Binary model ( SEW ) captures motion based on edge information and the Silhouette Skeleton Wavelet model ( SSW ) captures motion based on skeleton movement . The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique . paragraph : Image alignment Image alignment is a key step in many computer vision tasks , including face verification , motion analysis , stereo matching , and object recognition . Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose . Spatio - temporal filters . Burt and Adelson lay out the theory of spatio - temporal models for motion estimation and Heeger provides a computational embodiment . While inspired by human perception , such methods did not perform well at the time . Marker-based based motion capture is the prevalent technique for estimating human motion . A common problem with the approach is the occlusion and mis-labeling of the markers ; typically the data requires tedious manual cleaning in post processing . With the advent of marker-based motion capture , attempts have been made to recognise and quantify attributes of “ type ” , “ content ” and “ behaviour ” from the motion data . Current work exists to obtain quick and easy identification of human motion for use in multiple settings , such as healthcare and gaming by using activity monitors , wearable technology and low-cost accelerometers . The analysis of deformable objects which have a high-degree of freedom has long been encouraged by numerous researchers because it can be applied to such diverse areas as medical engineering , video surveillance and monitoring , Human Computer Interaction , browsing of video databases , interactive gaming and other growing applications . Within the computerized environments , the systems are largely separated into marker based motion capture and markerless motion capture . In particular , markerless motion capture and analysis have also been heavily studied by numerous researchers using local features , color , shape , texture , and depth map from stereo vision , but it is still a challenging issue in the area of computer vision and computer graphics due to partial occlusion , clutter , dependency of camera viewpoints , high-dimensional state space and pose ambiguity within the target object . In this thesis , we address the issue of the efficient markerless motion capture and representation methodology using skeletal features for the purpose of analysis and recognition of their motion patterns in video sequences . The main problem of markerless human motion capture is the high-dimensional search space . Tracking approaches therefore utilize temporal information and rely on the pose differences between consecutive frames being small . We present a system for markerless human motion capture through a hierarchical method from multiple camera views . In the absence of markers , the task of recovering the human pose is challenging and requires strong image features and robust algorithm . Using additional cameras , this approach could also be used to perform 3D analyses . The method presented here may have broad applications in different fields , for example by enabling markerless motion analysis to be performed during rehabilitation , training or even competition environments . Posture data from subsequent movements estimated by the built-in skeleton tracker in the Kinect system were further improved by fitting each body segment of the manikin to a corresponding partial depth dataset while the segment lengths were preserved as defined in the manikin . This study demonstrated how a model-based approach can improve the accuracy and feasibility of a depth camera system so that the system can be readily applied for various applications , including in-vehicle occupant dynamic analysis , occupant classification , and markerless motion analysis . A hierarchical skeleton template is frequently used to model the human body in literature , because it simplifies markerless human motion capture as a problem of estimating the human body shape and joint angle parameters . The proposed work establishes a skeleton based markerless human motion capture framework , comprising of ( 1 ) an improved deformation skin model suitable for markerless motion capture while it is compliant with the computer animation standard , ( 2 ) image segmentation by using Gaussian mixture static background subtraction and ( 3 ) nonlinear dynamic temporal tracking with annealed particle filter . This framework is able to efficiently represent markerless human motion capture as an optimisation problem in the temporal domain and solve it by the classic optimisation scheme . ( Applications de la topologie discrète pour la captation de mouvement temps réel et sans marqueurs ) . This manuscript deals with the problem of markerless motion capture . An approach to thisproblem is model-based and is divided into two steps : an initialization step in which the initialpose is estimated , and a tracking which computes the current pose of the subject using infor-mation of previous ones . However , the accuracy and practicality of these systems has yet to be fully scrutinised , meaning such markerless systems are not currently in widespread use within biomechanics . ConclusionsThis review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics , while considering the challenges with accuracy and robustness that are yet to be addressed . This paper presents an approach for markerless vision-based motion capture from multiple views . We use truncated cones to describe the human body parts , and match the extremal contours of human body model against the image cues . It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device . The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking , as required , for example , in activity recognition , physical/athletic performance evaluation and rehabilitation . However , conventional approaches such as stochastic particle filtering have shortcomings in computational cost , slowness of convergence , suffers from the curse of dimensionality and demand a high number of evaluations to achieve accurate results . Particle swarm optimization ( PSO ) is a population-based globalized search algorithm which has been successfully applied to address human motion tracking problem and produced better results in high-dimensional search space . This paper presents a systematic literature survey on the PSO algorithm and its variants to human motion tracking . While many others use stochastic sampling for model-based motion tracking , our method is purely dependent on parameter optimization . We convert the human motion tracking problem into a parameter optimization problem . A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette . The captured knowledge is then used by the system to assist and train inexperienced operators . The overall approach is based on state-of-the-art techniques in motion and object tracking , task analysis , decision-making and user-adaptive visualisation by means of augmented reality . The COGNITO system is an important step towards cognitive operator support . In particular , we modify QSRLib , a library that allows computation of Qualitative Spatial Relations and Calculi , and employ it for feature extraction , before inputting features into our neural network models . Using an experimental setup involving motion captures of human-object interaction as three dimensional inputs , we observe that the use of qualitative spatial features significantly improves the performance of our machine learning algorithm against our baseline , while quantitative features of similar kinds fail to deliver similar improvement . We also observe that sequential representations of QSR features yield the best classification performance . The invention proposes an algorithm of video pedestrian face detection and tracking through algorithms of motion object detection and motion object tracking , which pertains to the technical field of pattern recognition . The invention is composed of two main parts of a pedestrian face detection algorithm based on the motion object detection and a pedestrian face tracking algorithm based on the motion object tracking . Motion object tracking is an important issue in computer vision . In this paper , a robust tracking algorithm based on multiple instance learning ( MIL ) is proposed . Recognition of human interactions in a video is useful for video annotation , automated surveillance , and content-based video retrieval . This paper presents a model-based approach to motion tracking and recognition of human interactions using multi-layer finite state automata ( FA ) . The system is used for widely-available , static-background monocular surveillance videos . Recognizing objects and persons , and tracking their movements in complex real scenes by using a sequence of images are among the most difficult tasks in computer vision . Object and human motion tracking in 3D real scenes can be achieved by means of Kahnan filtering — a suitable mathematical model for describing objects and persons — and a refined dynamic model for tracking them while moving and reciprocally interacting is needed . Such approaches can provide accurate and robust results even in uncontrolled real-life working conditions . However , such a criterion often misses the true motion vectors , due to many practical factors , e.g. affine warping , image noise , object occlusion , lighting variation , and existence of multiple minimal DFD . Our goal is to find motion vectors of the features for object-based motion tracking , in which ( 1 ) any region of an object contains a good number of blocks , whose motion vectors exhibit certain consistency ; and ( 2 ) only true motion vectors for a few blocks per region are needed . Hence , we propose a new tracking method . This paper is concerned with a differential motion detection technique in log-polar coordinates which allows object motion tracking independently of the camera ego-motion when camera focus is along the movement direction . The method does not use any explicit estimation of the motion field , which can be calculated afterwards at the moving points . In this paper two complementary approaches for object motion tracking are presented . The objects considered , highly deformable , are rain cells issued form weather radar image sequences . With highly reliable inertial data , PAMS is designed for a spectrum of applications in healthcare monitoring , electronic entertainment and biokinetics researches . Case studies of real-time human motion tracking via PAMS are demonstrated and performances are evaluated . The selected-region tracking , combined with human-body structural data , restricts the temporal interpretation of 3D human poses to those best corresponding to the 2D silhouette shapes . The experimental results demonstrate that our approach performs real-time human-motion tracking with good quality and reasonable robustness . ( Algorithms for conserved markers sequences reconstruction in metagenomics data ) . Les progres recents en termes de sequencage d’ADN permettent maintenant d’acceder au materiel genetique de communautes microbiennes extraites directement d’echantillons environnementaux naturels . Conclusions : The proposed algorithm provides a rational methodology of informative marker selection , allowing for control and optimisation of information content and full marker reconstruction . Moreover , the reconstruction step can also be applied to tag markers selected using a different method at the stage of study design , identifying those markers which can not be uniquely recovered from the chosen tags . Relevant contributions dealing with intrinsic sources of systematic and random errors , such as the issues concerning camera calibration and filtering and smoothing of marker position data , are presented . Procedures for marker imaged processing , and missing marker recovery are also surveyed . Methods for checking the accuracy and precision of stereophotogrammetric systems are then reviewed .
The topic of our research is to explain how we can use new technology to transform traditional contents into popular media format while preserving the originality of the art and culture . We found a process to integrate art , culture , woodcut print and folk dance with technology and animation , by using motion capture , 3d animation and photo retouch technology .
Motion capture remains a popular and widely-used method for animating virtual characters . However , all practical applications of motion capture rely on motion editing techniques to increase the reusability and flexibility of captured motions . Because humans are proficient in detecting and interpreting subtle details in human motion , understanding the perceptual consequences of motion editing is essential .
The fiber orientations were automatically computed , while the muscle line of action was determined using approaches based on anatomical landmarks and on centroids of image segmentation . Three healthy male volunteers were recruited for MRI scanning and motion capture acquisition . This work evaluates the effect of subject-specific pennation angle as musculoskeletal parameter in the lower limb , focusing on the quadriceps group .
Motion capture analysis of sit-to-stand task with assistive device is hard to achieve due to obstruction on reflective makers . Previously developed robotic system , Smart Mobile Walker , is used as an assistive device to perform motion capture analysis in sit-to-stand task . All lower limb markers except hip markers are invisible through whole session .
Moreover , it is low cost , portable and easy to use . As a conclusion , the current study and its findings are found useful and has contributed to enhance significant knowledge pertaining to motion capture-analysis , drop jump movement and HD VideoCam – Kinovea integration .
Experiments show that our method not only has a good performance in behavioral segmentation for motion capture data , but also finds cycles of motion and the motion clips of the same behaviors from long original motion sequence . Keywords-motion analysis ; behavioral segmentation ; clustering ; motion capture data ; cycle
In the preliminary works the flexion-extension of the elbow joint only is considered . Experimental set-up includes EMGs acquisition and motion capture data . Movements are chosen to limit cocontraction of antagonist muscles .
We present an animation interface designed to conveniently control the motion capture process . The transition of performer 's hand gestures tracked by a dataglove is recognized for software remote control .
Reconstruction is a key step of the motion capture process . The quality of motion data first results from the quality of raw data .
Vision-based object tracking has lots of applications in robotics , like surveillance , navigation , motion capturing , and so on . However , the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms .
Simulated annealing , on the other hand , is a global optimization method derived from statistical mechanics . A recent heuristic approach to fuse these two techniques for motion capturing has become known as annealed particle filter . In order to analyze these techniques , we rigorously derive in this paper two algorithms with annealing properties based on the mathematical theory of interacting particle systems .
The Acrobot model , its physical structure , the degree of freedom and the technical requirements of International Humanoid-robot Olympic Games(IHOG ) were analyzed for the robot on a horizontal bar . The key steps of this dynamic motion strategy are motion data capture through the video of human 's motion on the horizontal bar , the key feature points ' analysis of the humanoid robot model and the joint angle 's kinematic data match in the basic movements . After making the appropriate adjustments due to the kinematic constraints , an interpolation method was used to generate the motion trajectory of all the joints .
The International Society of Biomechanics ( ISB ) has recommended a standardization of the definition of the joint coordinate system ( JCS ) and use of a sequential rotation to describe human shoulder joint rotation . Markers attached to the surface of the body may move during the process of motion data capture , resulting in an offset from their initial location . This leads to a change of the JCS and therefore affects the calculated shoulder joint angles .
The methodology applies background extraction using the Gaussian Mixture Model ( GMM ) , a scale reduction based on the Wavelet Transform ( WT ) and feature extraction by Principal Component Analysis ( PCA ) . We propose four new schemas for motion information capture : the Silhouette-Gray-Wavelet model ( SGW ) captures motion based on grey level variations ; the Silhouette-Binary-Wavelet model ( SBW ) captures motion based on binary information ; the Silhouette – Edge-Binary model ( SEW ) captures motion based on edge information and the Silhouette Skeleton Wavelet model ( SSW ) captures motion based on skeleton movement . The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique .
paragraph : Image alignment Image alignment is a key step in many computer vision tasks , including face verification , motion analysis , stereo matching , and object recognition . Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose .
Spatio - temporal filters . Burt and Adelson lay out the theory of spatio - temporal models for motion estimation and Heeger provides a computational embodiment . While inspired by human perception , such methods did not perform well at the time .
Marker-based based motion capture is the prevalent technique for estimating human motion . A common problem with the approach is the occlusion and mis-labeling of the markers ; typically the data requires tedious manual cleaning in post processing .
With the advent of marker-based motion capture , attempts have been made to recognise and quantify attributes of “ type ” , “ content ” and “ behaviour ” from the motion data . Current work exists to obtain quick and easy identification of human motion for use in multiple settings , such as healthcare and gaming by using activity monitors , wearable technology and low-cost accelerometers .
The analysis of deformable objects which have a high-degree of freedom has long been encouraged by numerous researchers because it can be applied to such diverse areas as medical engineering , video surveillance and monitoring , Human Computer Interaction , browsing of video databases , interactive gaming and other growing applications . Within the computerized environments , the systems are largely separated into marker based motion capture and markerless motion capture . In particular , markerless motion capture and analysis have also been heavily studied by numerous researchers using local features , color , shape , texture , and depth map from stereo vision , but it is still a challenging issue in the area of computer vision and computer graphics due to partial occlusion , clutter , dependency of camera viewpoints , high-dimensional state space and pose ambiguity within the target object . In this thesis , we address the issue of the efficient markerless motion capture and representation methodology using skeletal features for the purpose of analysis and recognition of their motion patterns in video sequences .
The main problem of markerless human motion capture is the high-dimensional search space . Tracking approaches therefore utilize temporal information and rely on the pose differences between consecutive frames being small .
We present a system for markerless human motion capture through a hierarchical method from multiple camera views . In the absence of markers , the task of recovering the human pose is challenging and requires strong image features and robust algorithm .
Using additional cameras , this approach could also be used to perform 3D analyses . The method presented here may have broad applications in different fields , for example by enabling markerless motion analysis to be performed during rehabilitation , training or even competition environments .
Posture data from subsequent movements estimated by the built-in skeleton tracker in the Kinect system were further improved by fitting each body segment of the manikin to a corresponding partial depth dataset while the segment lengths were preserved as defined in the manikin . This study demonstrated how a model-based approach can improve the accuracy and feasibility of a depth camera system so that the system can be readily applied for various applications , including in-vehicle occupant dynamic analysis , occupant classification , and markerless motion analysis .
A hierarchical skeleton template is frequently used to model the human body in literature , because it simplifies markerless human motion capture as a problem of estimating the human body shape and joint angle parameters . The proposed work establishes a skeleton based markerless human motion capture framework , comprising of ( 1 ) an improved deformation skin model suitable for markerless motion capture while it is compliant with the computer animation standard , ( 2 ) image segmentation by using Gaussian mixture static background subtraction and ( 3 ) nonlinear dynamic temporal tracking with annealed particle filter . This framework is able to efficiently represent markerless human motion capture as an optimisation problem in the temporal domain and solve it by the classic optimisation scheme .
( Applications de la topologie discrète pour la captation de mouvement temps réel et sans marqueurs ) . This manuscript deals with the problem of markerless motion capture . An approach to thisproblem is model-based and is divided into two steps : an initialization step in which the initialpose is estimated , and a tracking which computes the current pose of the subject using infor-mation of previous ones .
However , the accuracy and practicality of these systems has yet to be fully scrutinised , meaning such markerless systems are not currently in widespread use within biomechanics . ConclusionsThis review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics , while considering the challenges with accuracy and robustness that are yet to be addressed .
This paper presents an approach for markerless vision-based motion capture from multiple views . We use truncated cones to describe the human body parts , and match the extremal contours of human body model against the image cues .
It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device . The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking , as required , for example , in activity recognition , physical/athletic performance evaluation and rehabilitation .
However , conventional approaches such as stochastic particle filtering have shortcomings in computational cost , slowness of convergence , suffers from the curse of dimensionality and demand a high number of evaluations to achieve accurate results . Particle swarm optimization ( PSO ) is a population-based globalized search algorithm which has been successfully applied to address human motion tracking problem and produced better results in high-dimensional search space . This paper presents a systematic literature survey on the PSO algorithm and its variants to human motion tracking .
While many others use stochastic sampling for model-based motion tracking , our method is purely dependent on parameter optimization . We convert the human motion tracking problem into a parameter optimization problem . A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette .
The captured knowledge is then used by the system to assist and train inexperienced operators . The overall approach is based on state-of-the-art techniques in motion and object tracking , task analysis , decision-making and user-adaptive visualisation by means of augmented reality . The COGNITO system is an important step towards cognitive operator support .
In particular , we modify QSRLib , a library that allows computation of Qualitative Spatial Relations and Calculi , and employ it for feature extraction , before inputting features into our neural network models . Using an experimental setup involving motion captures of human-object interaction as three dimensional inputs , we observe that the use of qualitative spatial features significantly improves the performance of our machine learning algorithm against our baseline , while quantitative features of similar kinds fail to deliver similar improvement . We also observe that sequential representations of QSR features yield the best classification performance .
The invention proposes an algorithm of video pedestrian face detection and tracking through algorithms of motion object detection and motion object tracking , which pertains to the technical field of pattern recognition . The invention is composed of two main parts of a pedestrian face detection algorithm based on the motion object detection and a pedestrian face tracking algorithm based on the motion object tracking .
Motion object tracking is an important issue in computer vision . In this paper , a robust tracking algorithm based on multiple instance learning ( MIL ) is proposed .
Recognition of human interactions in a video is useful for video annotation , automated surveillance , and content-based video retrieval . This paper presents a model-based approach to motion tracking and recognition of human interactions using multi-layer finite state automata ( FA ) . The system is used for widely-available , static-background monocular surveillance videos .
Recognizing objects and persons , and tracking their movements in complex real scenes by using a sequence of images are among the most difficult tasks in computer vision . Object and human motion tracking in 3D real scenes can be achieved by means of Kahnan filtering — a suitable mathematical model for describing objects and persons — and a refined dynamic model for tracking them while moving and reciprocally interacting is needed . Such approaches can provide accurate and robust results even in uncontrolled real-life working conditions .
However , such a criterion often misses the true motion vectors , due to many practical factors , e.g. affine warping , image noise , object occlusion , lighting variation , and existence of multiple minimal DFD . Our goal is to find motion vectors of the features for object-based motion tracking , in which ( 1 ) any region of an object contains a good number of blocks , whose motion vectors exhibit certain consistency ; and ( 2 ) only true motion vectors for a few blocks per region are needed . Hence , we propose a new tracking method .
This paper is concerned with a differential motion detection technique in log-polar coordinates which allows object motion tracking independently of the camera ego-motion when camera focus is along the movement direction . The method does not use any explicit estimation of the motion field , which can be calculated afterwards at the moving points .
In this paper two complementary approaches for object motion tracking are presented . The objects considered , highly deformable , are rain cells issued form weather radar image sequences .
With highly reliable inertial data , PAMS is designed for a spectrum of applications in healthcare monitoring , electronic entertainment and biokinetics researches . Case studies of real-time human motion tracking via PAMS are demonstrated and performances are evaluated .
The selected-region tracking , combined with human-body structural data , restricts the temporal interpretation of 3D human poses to those best corresponding to the 2D silhouette shapes . The experimental results demonstrate that our approach performs real-time human-motion tracking with good quality and reasonable robustness .
( Algorithms for conserved markers sequences reconstruction in metagenomics data ) . Les progres recents en termes de sequencage d’ADN permettent maintenant d’acceder au materiel genetique de communautes microbiennes extraites directement d’echantillons environnementaux naturels .
Conclusions : The proposed algorithm provides a rational methodology of informative marker selection , allowing for control and optimisation of information content and full marker reconstruction . Moreover , the reconstruction step can also be applied to tag markers selected using a different method at the stage of study design , identifying those markers which can not be uniquely recovered from the chosen tags .
Relevant contributions dealing with intrinsic sources of systematic and random errors , such as the issues concerning camera calibration and filtering and smoothing of marker position data , are presented . Procedures for marker imaged processing , and missing marker recovery are also surveyed . Methods for checking the accuracy and precision of stereophotogrammetric systems are then reviewed .
White Crane Dance-Transforming Woodcut Print and Folk Dance into Animation Art The effect of posture and dynamics on the perception of emotion A computational approach to calculate personalized pennation angle based on MRI: effect on motion analysis Hidden marker position estimation during sit-to-stand with walker Biomechanical analysis using Kinovea for sports application A Symbolic Representation of Motion Capture Data for Behavioral Segmentation Identifying musculo-tendon parameters of human body based on the musculo-skeletal dynamics computation and Hill-Stroeve muscle model An animation interface designed for motion capture Reconstructing Motion Capture Data for Human Crowd Study Real-time object tracking system based on field-programmable gate array and convolution neural network Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications HMCD-based dynamic motion control strategy of humanoid robot on a horizontal bar Shoulder joint angle errors caused by marker offset Human gait recognition using extraction and fusion of global motion features Do Convnets Learn Correspondence? Optical Flow Estimation Using a Spatial Pyramid Network Real-Time Unlabeled Marker Pose Estimation via Constrained Extended Kalman Filter 3D human action recognition and motion analysis using selective representations Markerless motion analysis in diffusion tensor fields and its applications Robust real-time stereo-based markerless human motion capture A New Hierarchical Method for Markerless Human Pose Estimation Markerless 2D kinematic analysis of underwater running: A deep learning approach. A Model-based Approach to Rapid Estimation of Body Shape and Postures Using Low-Cost Depth Cameras Multi-view Human Motion Capture with an Improved Deformation Skin Model Applications of digital topology for real-time markerless motion capture. (Applications de la topologie discrète pour la captation de mouvement temps réel et sans marqueurs) A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System Markerless human pose estimation using image features and extremal contour Design of a Wearable Sensing System for Human Motion Monitoring in Physical Rehabilitation A Review on Particle Swarm Optimization Algorithm and Its Variants to Human Motion Tracking Human Motion Tracking by Combining View-Based and Model-Based Methods for Monocular Video Sequences COGNITO: a cognitive assistance and training system for manual tasks in industry Learning event representation: As sparse as possible, but not sparser Passerby face detection and tracing algorithm based on video Visual tracking via online discriminative multiple instance metric learning Video Retrieval of Human Interactions Using Model-Based Motion Tracking and Multi-layer Finite State Automata “Long-Memory” Matching of Interacting Complex Objects from Real Image Sequences A feature tracking algorithm using neighborhood relaxation with multi-candidate pre-screening Detecting Motion Independent of the Camera Movement Through a Log-Polar Differential Approach Combinatorial optimization and linear prediction approaches to rain cell tracking PAMS: a wearable physical activity monitoring system for continuous motion capture in free-living environments 3D Human-Pose Tracking through a Monocular Vision Algorithmes pour la reconstruction de séquences de marqueurs conservés dans des données de métagénomique. (Algorithms for conserved markers sequences reconstruction in metagenomics data) Individual SNP Allele Reconstruction from Informative Markers Selected by a Non-Linear Gauss-Type Algorithm Human movement analysis using stereophotogrammetry. Part 2: instrumental errors.
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HCI (4) SAP EMBC DMS Humanoids CA MIG NIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ISER Humanoids ICVS DICTA ICCSA (3) ECCE CIVR ICIP (2) CAIP BODYNETS RiTA
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