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Опубліковано: 11 вересня 2023

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Статті в журналах з теми "TRACKING THE HUMAN BODY"

1

Zhang, Gang, Bin Ouyang, Lu Ming Yu, and Lei Zhang. "Research of Human Body Detection and Tracking Algorithm." Advanced Materials Research 791-793 (September 2013): 1023–27. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1023.

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In this paper, the proposed algorithm regards the human body object character symbol using Support Vector Machine (SVM) classifier to train and classify Histogram of Oriented Gradient (HOG) features, which improve the accuracy of human body detection. We use optical flow tracking algorithm based on corner points of the contour for tracking. Kalman filter is regarded as the predictor to predict the size and location of the searching object. Also, the size and location of track window is real-time updated. In this paper, we present an object tracking algorithm for multi-media teaching video shoot. Target tracking technology is used for the video image processing analysis. By extracting moving object, we can get information in the subsequent frames to determine the trajectory and size of moving objects. After analysis of a large number of experiments, we can draw the conclusion that the algorithm is effective.
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2

Wren, C. R., A. Azarbayejani, T. Darrell, and A. P. Pentland. "Pfinder: real-time tracking of the human body." IEEE Transactions on Pattern Analysis and Machine Intelligence 19, no. 7 (July 1997): 780–85. http://dx.doi.org/10.1109/34.598236.

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3

Jang, Dae-Sik, Seok-Woo Jang, and Hyung-Il Choi. "2D human body tracking with Structural Kalman filter." Pattern Recognition 35, no. 10 (October 2002): 2041–49. http://dx.doi.org/10.1016/s0031-3203(01)00201-1.

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4

Yu, Jie, FengLi Zhang, Jian Xiong, and GuoCheng Yang. "A Robust Real-Time Human Body Fuzzy Tracking Based Face Tracking Algorithm." Journal of Computational and Theoretical Nanoscience 12, no. 12 (December 1, 2015): 5728–38. http://dx.doi.org/10.1166/jctn.2015.4709.

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5

Wang, Jun Jie. "The Visual Simulation Analysis of Human Body Movement Model." Applied Mechanics and Materials 556-562 (May 2014): 3913–16. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3913.

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This paper proposes the re-built human body movement model with multiple cameras. In the tracking frame of the non-linear optimization strategy, the paper builds the body dynamic model to dynamically simulate the human movement which effectively solves the issues of the body parts overlap and tracking errors accumulate. Compared with traditional methods, the required equipment is very economic and the matching accuracy of the algorithm is quite high. The paper applies the athletes as the experimental examples which illustrate the proposed algorithm can effectively increase the 3D image tracking matching accuracy in dynamic videos as the analysis basis.
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6

Polat, Ediz, Mohammed Yeasin, and Rajeev Sharma. "Robust tracking of human body parts for collaborative human computer interaction." Computer Vision and Image Understanding 89, no. 1 (January 2003): 44–69. http://dx.doi.org/10.1016/s1077-3142(02)00031-0.

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7

KHONGKRAPHAN, Kittiya, and Pakorn KAEWTRAKULPONG. "Efficient Human Body Tracking by Quick Shift Belief Propagation." IEICE Transactions on Information and Systems E94-D, no. 4 (2011): 905–12. http://dx.doi.org/10.1587/transinf.e94.d.905.

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8

Zhou, Yi. "Bayesian variational human tracking based on informative body parts." Optical Engineering 51, no. 6 (June 5, 2012): 067203. http://dx.doi.org/10.1117/1.oe.51.6.067203.

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9

Herda, L., R. Urtasun, and P. Fua. "Hierarchical implicit surface joint limits for human body tracking." Computer Vision and Image Understanding 99, no. 2 (August 2005): 189–209. http://dx.doi.org/10.1016/j.cviu.2005.01.005.

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10

Cao, Xiao-Qin, and Zhi-Qiang Liu. "Sequential Markov random fields for human body parts tracking." Multimedia Tools and Applications 74, no. 17 (May 14, 2014): 6671–90. http://dx.doi.org/10.1007/s11042-014-1924-3.

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Дисертації з теми "TRACKING THE HUMAN BODY"

1

Topcu, Hasan Huseyin. "Human Body Part Detection And Multi-human Tracking Insurveillance Videos." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614308/index.pdf.

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With the recent developments in Computer Vision and Pattern Recognition, surveillance applications are equipped with the capabilities of event/activity understanding and interpretation which usually require recognizing humans in real world scenes. Real world scenes such as airports, streets and train stations are complex because they involve many people, complicated occlusions and cluttered backgrounds. Although complex real world scenes exist, human detectors have the capability to locate pedestrians accurately even in complex scenes and visual trackers have the capability to track targets in cluttered environments. The integration of visual object detection and tracking, which are the fundamental features of available surveillance applications, is one of the solutions for multi-human tracking problem in crowded scenes which is studied in this thesis. In this thesis, human body part detectors, which are capable of detecting human heads and human upper body parts, are trained with Support Vector Machines (SVM) by using Histogram of Oriented Gradients (HOG), which is one of the state-of-the-art descriptor for human detection. The training process is elaborated by investigating the effects of the parameters of the HOG descriptor. The human heads and upper body parts are searched in the region of interests (ROI) computed by detecting motion. In addition, these human body part detectors are integrated with a multi-human tracker which solves the data association problem with the Multi Scan Markov Chain Monte Carlo Data Association (MCMCDA) algorithm. Associated measurements of human upper body part locations are used for state correction for each track. State estimation is done through Kalman Filter. The performance of detectors are evaluated using MIT Pedestrian dataset and INRIA Human dataset.
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2

Wren, Christopher R. (Christopher Richard). "Pfinder : real-time tracking of the human body." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10652.

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3

Bao, Guanqun. "On Simultaneous Localization and Mapping inside the Human Body (Body-SLAM)." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/206.

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Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE's embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm.
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4

Zhang, Qing. "HIGH QUALITY HUMAN 3D BODY MODELING, TRACKING AND APPLICATION." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/39.

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Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability. In this dissertation, a complete process is designed from geometric modeling detailed 3D human full body and capturing shape dynamics over time using a flexible setup to guiding clothes/person re-targeting with such data-driven models. As the mechanical movement of human body can be considered as an articulate motion, which is easy to guide the skin animation but has difficulties in the reverse process to find parameters from images without manual intervention, we present a novel parametric model, GMM-BlendSCAPE, jointly taking both linear skinning model and the prior art of BlendSCAPE (Blend Shape Completion and Animation for PEople) into consideration and develop a Gaussian Mixture Model (GMM) to infer both body shape and pose from incomplete observations. We show the increased accuracy of joints and skin surface estimation using our model compared to the skeleton based motion tracking. To model the detailed body, we start with capturing high-quality partial 3D scans by using a single-view commercial depth camera. Based on GMM-BlendSCAPE, we can then reconstruct multiple complete static models of large pose difference via our novel non-rigid registration algorithm. With vertex correspondences established, these models can be further converted into a personalized drivable template and used for robust pose tracking in a similar GMM framework. Moreover, we design a general purpose real-time non-rigid deformation algorithm to accelerate this registration. Last but not least, we demonstrate a novel virtual clothes try-on application based on our personalized model utilizing both image and depth cues to synthesize and re-target clothes for single-view videos of different people.
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5

Renna, I. "Upper body tracking and Gesture recognition for Human-Machine Interaction." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00717443.

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Les robots sont des agents artificiels qui peuvent agir dans le monde des humains grâce aux capacités de perception. Dans un contexte d'interaction homme-robot, les humains et les robots partagent le même espace de communication. En effet, les robots compagnons sont censés communiquer avec les humains d'une manière naturelle et intuitive: l'une des façons les plus naturelles est basée sur les gestes et les mouvements réactifs du corps. Pour rendre cette interaction la plus conviviale possible, un robot compagnon doit, donc, être doté d'une ou plusieurs capacités lui permettant de percevoir, de reconnaître et de réagir aux gestes humains. Cette thèse a été focalisée sur la conception et le développement d'un système de reconnaissance gestuelle dans un contexte d'interaction homme-robot. Ce système comprend un algorithme de suivi permettant de connaître la position du corps lors des mouvements et un module de niveau supérieur qui reconnaît les gestes effectués par des utilisateurs humains. De nouvelles contributions ont été apportées dans les deux sujets. Tout d'abord, une nouvelle approche est proposée pour le suivi visuel des membres du haut du corps. L'analyse du mouvement du corps humain est difficile, en raison du nombre important de degrés de liberté de l'objet articulé qui modélise la partie supérieure du corps. Pour contourner la complexité de calcul, chaque membre est suivi avec un filtre particulaire à recuit simulé et les différents filtres interagissent grâce à la propagation de croyance. Le corps humain en 3D est ainsi qualifié comme un modèle graphique dans lequel les relations entre les parties du corps sont représentées par des distributions de probabilité conditionnelles. Le problème d'estimation de la pose est donc formulé comme une inférence probabiliste sur un modèle graphique, où les variables aléatoires correspondent aux paramètres des membres individuels (position et orientation) et les messages de propagation de croyance assurent la cohérence entre les membres. Deuxièmement, nous proposons un cadre permettant la détection et la reconnaissance des gestes emblématiques. La question la plus difficile dans la reconnaissance des gestes est de trouver de bonnes caractéristiques avec un pouvoir discriminant (faire la distinction entre différents gestes) et une bonne robustesse à la variabilité intrinsèque des gestes (le contexte dans lequel les gestes sont exprimés, la morphologie de la personne, le point de vue, etc). Dans ce travail, nous proposons un nouveau modèle de normalisation de la cinématique du bras reflétant à la fois l'activité musculaire et l'apparence du bras quand un geste est effectué. Les signaux obtenus sont d'abord segmentés et ensuite analysés par deux techniques d'apprentissage : les chaînes de Markov cachées et les Support Vector Machine. Les deux méthodes sont comparées dans une tâche de reconnaissance de 5 classes de gestes emblématiques. Les deux systèmes présentent de bonnes performances avec une base de données de formation minimaliste quels que soient l'anthropométrie, le sexe, l'âge ou la pose de l'acteur par rapport au système de détection. Le travail présenté ici a été réalisé dans le cadre d'une thèse de doctorat en co-tutelle entre l'Université "Pierre et Marie Curie" (ISIR laboratoire, Paris) et l'Université de Gênes (IIT - Tera département) et a été labelisée par l'Université Franco-Italienne.
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6

Renna, Ilaria. "Upper body tracking and Gesture recognition for Human-Machine Interaction." Paris 6, 2012. http://www.theses.fr/2012PA066119.

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Анотація:
Les robots sont des agents artificiels qui peuvent agir dans le monde des humains grâce aux capacités de perception. Dans un contexte d’interaction homme-robot, les humains et les robots partagent le même espace de communication. En effet, les robots compagnons sont censés communiquer avec les humains d’une manière naturelle et intuitive: l’une des façons les plus naturelles est basée sur les gestes et les mouvements réactifs du corps. Pour rendre cette interaction la plus conviviale possible, un robot compagnon doit, donc, être doté d’une ou plusieurs capacités lui permettant de percevoir, de reconnaître et de réagir aux gestes humains. Cette thèse a été focalisée sur la conception et le développement d’un système de reconnaissance gestuelle dans un contexte d’interaction homme-robot. Ce système comprend un algorithme de suivi permettant de connaître la position du corps lors des mouvements et un module de niveau supérieur qui reconnaît les gestes effectués par des utilisateurs humains. De nouvelles contributions ont été apportées dans les deux sujets. Tout d’abord, une nouvelle approche est proposée pour le suivi visuel des membres du haut du corps. L’analyse du mouvement du corps humain est difficile, en raison du nombre important de degrés de liberté de l’objet articulé qui modélise la partie supérieure du corps. Pour contourner la complexité de calcul, chaque membre est suivi avec un filtre particulaire à recuit simulé et les différents filtres interagissent grâce à la propagation de croyance. Le corps humain en 3D est ainsi qualifié comme un modèle graphique dans lequel les relations entre les parties du corps sont représentées par des distributions de probabilité conditionnelles. Le problème d’estimation de la pose est donc formulé comme une inférence probabiliste sur un modèle graphique, où les variables aléatoires correspondent aux paramètres des membres individuels (position et orientation) et les messages de propagation de croyance assurent la cohérence entre les membres. Deuxièmement, nous proposons un cadre permettant la détection et la reconnais- sance des gestes emblématiques. La question la plus difficile dans la reconnaissance des gestes est de trouver de bonnes caractéristiques avec un pouvoir discriminant (faire la distinction entre différents gestes) et une bonne robustesse à la variabilité intrinsèque des gestes (le contexte dans lequel les gestes sont exprimés, la morpholo- gie de la personne, le point de vue, etc). Dans ce travail, nous proposons un nouveau modèle de normalisation de la cinématique du bras reflétant à la fois l’activité mus- culaire et l’apparence du bras quand un geste est effectué. Les signaux obtenus sont d’abord segmentés et ensuite analysés par deux techniques d’apprentissage : les chaînes de Markov cachées et les Support Vector Machine. Les deux méthodes sont comparées dans une tâche de reconnaissance de 5 classes de gestes emblématiques. Les deux systèmes présentent de bonnes performances avec une base de données de formation minimaliste quels que soient l’anthropométrie, le sexe, l’âge ou la pose de l’acteur par rapport au système de détection. Le travail présenté ici a été réalisé dans le cadre d’une thèse de doctorat en co-tutelle entre l’Université “Pierre et Marie Curie” (ISIR laboratoire, Paris) et l’Université de Gênes (IIT - Tera département) et a été labelisée par l’Université Franco-Italienne
Robots are artificial agents that can act in humans’ world thanks to perception, action and reasoning capacities. In particular, robots companion are designed to share with humans the same physical and communication spaces in performing daily life collaborative tasks and aids. In such a context, interactions between humans and robots are expected to be as natural and as intuitive as possible. One of the most natural ways is based on gestures and reactive body motions. To make this friendly interaction possible, a robot companion has to be endowed with one or more capabilities allowing him to perceive, to recognize and to react to human gestures. This PhD thesis has been focused on the design and the development of a gesture recognition system that can be exploited in a human-robot interaction context. This system includes (1) a limbs-tracking algorithm that determines human body position during movements and (2) a higher-level module that recognizes gestures performed by human users. New contributions were made in both topics. First, a new approach is proposed for visual tracking of upper-body limbs. Analysing human body motion is challenging, due to the important number of degrees of freedom of the articulated object modelling the upper body. To circumvent the computational complexity, each limb is tracked with an Annealed Particle Filter and the different filters interact through Belief Propagation. 3D human body is described as a graphical model in which the relationships between the body parts are represented by conditional probability distributions. Pose estimation problem is thus formulated as a probabilistic inference over a graphical model, where the random variables correspond to the individual limb parameters (position and orientation) and Belief Propagation messages ensure coherence between limbs. Secondly, we propose a framework allowing emblematic gestures detection and recognition. The most challenging issue in gesture recognition is to find good features with a discriminant power (to distinguish between different gestures) and a good robustness to intrinsic gestures variability (the context in which gestures are expressed, the morphology of the person, the point of view, etc. ). In this work, we propose a new arm's kinematics normalization scheme reflecting both the muscular activity and arm's appearance when a gesture is performed. The obtained signals are first segmented and then analysed by two machine learning techniques: Hidden Markov Models and Support Vector Machines. The two methods are compared in a 5 classes emblematic gestures recognition task. Both systems show good performances with a minimalistic training database regardless to performer's anthropometry, gender, age or pose with regard to the sensing system. The work presented here has been done within the framework of a PhD thesis in joint supervision between the “Pierre et Marie Curie” University (ISIR laboratory, Paris) and the University of Genova (IIT--Tera department) and was labelled by the French-Italian University
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7

Lu, Yao. "Human body tracking and pose estimation from monocular image sequences." Thesis, Curtin University, 2013. http://hdl.handle.net/20.500.11937/1665.

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This thesis describes a bottom-up approach to estimating human pose over time based on monocular views with no restriction on human activities,Three approaches are proposed to address the weaknesses of existing approaches, including building a specific appearance model using clustering,utilising both the generic and specific appearance models in the estimation, and building an uncontaminated appearance model by removing backgroundpixels from the training samples. Experimental results show that the proposed system outperforms existing system significantly.
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8

Azhar, Faisal. "Marker-less human body part detection, labelling and tracking for human activity recognition." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/69575/.

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This thesis focuses on the development of a real-time and cost effective marker-less computer vision method for significant body point or part detection (i.e., the head, arm, shoulder, knee, and feet), labelling and tracking, and its application to activity recognition. This work comprises of three parts: significantbody point detection and labelling, significant body point tracking, and activity recognition. Implicit body models are proposed based on human anthropometry, kinesiology, and human vision inspired criteria to detect and label significant body points. The key idea of the proposed method is to fit the knowledge from the implicit body models rather than fitting the predefined models in order to detect and label significant body points. The advantages of this method are that it does not require manual annotation, an explicit fitting procedure, and a training (learning) phase, and it is applicable to humans with different anthropometric proportions. The experimental results show that the proposed method robustly detects and labels significant body points in various activities of two different (low and high) resolution data sets. Furthermore, a Particle Filter with memory and feedback is proposed that combines temporal information of the previous observation and estimation with feedback to track significant body points in occlusion. In addition, in order to overcome the problem presented by the most occluded body part, i.e., the arm, a Motion Flow method is proposed. This method considers the human arm as a pendulum attached to the shoulder joint and defines conjectures to track the arm since it is the most occluded body part. The former method is invoked as default and the latter is used as per a user's choice. The experimental results show that the two proposed methods, i.e., Particle Filter and Motion Flow methods, robustly track significant body points in various activities of the above-mentioned two data sets and also enhance the performance of significant body point detection. A hierarchical relaxed partitioning system is then proposed that employs features extracted from the significant body points for activity recognition when multiple overlaps exist in the feature space. The working principle of the proposed method is based on the relaxed hierarchy (postpone uncertain decisions) and hierarchical strategy (group similar or confusing classes) while partitioning each class at different levels of the hierarchy. The advantages of the proposed method lie in its real-time speed, ease of implementation and extension, and non-intensive training. The experimental results show that it acquires valuable features and outperforms relevant state-of-the-art methods while comparable to other methods, i.e., the holistic and local feature approaches. In this context, the contribution of this thesis is three-fold: Pioneering a method for automated human body part detection and labelling. Developing methods for tracking human body parts in occlusion. Designing a method for robust and efficient human action recognition.
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9

Abedan, Kondori Farid. "Bring Your Body into Action : Body Gesture Detection, Tracking, and Analysis for Natural Interaction." Doctoral thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-88508.

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Анотація:
Due to the large influx of computers in our daily lives, human-computer interaction has become crucially important. For a long time, focusing on what users need has been critical for designing interaction methods. However, new perspective tends to extend this attitude to encompass how human desires, interests, and ambitions can be met and supported. This implies that the way we interact with computers should be revisited. Centralizing human values rather than user needs is of the utmost importance for providing new interaction techniques. These values drive our decisions and actions, and are essential to what makes us human. This motivated us to introduce new interaction methods that will support human values, particularly human well-being. The aim of this thesis is to design new interaction methods that will empower human to have a healthy, intuitive, and pleasurable interaction with tomorrow’s digital world. In order to achieve this aim, this research is concerned with developing theories and techniques for exploring interaction methods beyond keyboard and mouse, utilizing human body. Therefore, this thesis addresses a very fundamental problem, human motion analysis. Technical contributions of this thesis introduce computer vision-based, marker-less systems to estimate and analyze body motion. The main focus of this research work is on head and hand motion analysis due to the fact that they are the most frequently used body parts for interacting with computers. This thesis gives an insight into the technical challenges and provides new perspectives and robust techniques for solving the problem.
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10

Fang, Bing. "A Framework for Human Body Tracking Using an Agent-based Architecture." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77135.

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Анотація:
The purpose of this dissertation is to present our agent-based human tracking framework, and to evaluate the results of our work in light of the previous research in the same field. Our agent-based approach departs from a process-centric model where the agents are bound to specific processes, and introduces a novel model by which agents are bound to the objects or sub-objects being recognized or tracked. The hierarchical agent-based model allows the system to handle a variety of cases, such as single people or multiple people in front of single or stereo cameras. We employ the job-market model for agents' communication. In this dissertation, we will present several experiments in detail, which demonstrate the effectiveness of the agent-based tracking system. Per our research, the agents are designed to be autonomous, self-aware entities that are capable of communicating with other agents to perform tracking within agent coalitions. Each agent with high-level abstracted knowledge seeks evidence for its existence from the low-level features (e.g. motion vector fields, color blobs) and its peers (other agents representing body-parts with which it is compatible). The power of the agent-based approach is its flexibility by which the domain information may be encoded within each agent to produce an overall tracking solution.
Ph. D.
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Книги з теми "TRACKING THE HUMAN BODY"

1

O’Hara, Kenton, Cecily Morrison, Abigail Sellen, Nadia Bianchi-Berthouze, and Cathy Craig. Body Tracking in Healthcare. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-01600-4.

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2

Lecos, Chris. Tracking trace minerals. Rockville, Md: Dept. of Health and Human Services, Public Health Service, Food and Drug Administration, 1985.

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3

Lecos, Chris. Tracking trace minerals. Rockville, Md: Dept. of Health and Human Services, Public Health Service, Food and Drug Administration, 1985.

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4

Arraj, Tyra. Tracking the elusive human. Chiloquin, OR: Inner Growth Books, 1988.

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5

Jim, Arraj, ed. Tracking the elusive human. Chiloquin, OR: Inner Growth Books, 1988.

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6

Steve, Parker. Human body. New York: DK Publishing, 2004.

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7

Parker, Steve. Human body. Thaxted: Miles Kelly, 2011.

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8

LaBat, Karen L., and Karen S. Ryan. Human Body. Boca : Taylor &Francis, 2019. | “A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.”: CRC Press, 2019. http://dx.doi.org/10.1201/9780429055690.

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Human body. Bristol: Parragon, 1996.

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Parker, Steve. Human body. Toronto: Stoddart, 1993.

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Частини книг з теми "TRACKING THE HUMAN BODY"

1

Nakano, Atsushi, and Junichi Hoshino. "Human Body Tracking for Digital Actors." In Entertainment Computing, 165–72. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35660-0_20.

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2

Hynes, Andrew, and Stephen Czarnuch. "Combinatorial Optimization for Human Body Tracking." In Advances in Visual Computing, 524–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50832-0_51.

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3

Lerasle, F., G. Rives, M. Dhome, and A. Yassine. "Human body tracking by monocular vision." In Lecture Notes in Computer Science, 518–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61123-1_166.

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4

Park, Jong-Seung, and Sang-Rak Lee. "Human Body Tracking for Human Computer Intelligent Interaction." In Entertainment Computing – ICEC 2004, 260–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28643-1_34.

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5

Lange, Christian, Thomas Hermann, and Helge Ritter. "Holistic Body Tracking for Gestural Interfaces." In Gesture-Based Communication in Human-Computer Interaction, 132–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24598-8_13.

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6

Zheng, Feng, Vitomir Racic, James M. W. Brownjohn, Mark T. Elliot, and Alan Wing. "Vision-Based Tracking of Human Body Motion." In Dynamics of Civil Structures, Volume 4, 171–74. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04546-7_20.

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7

Jesus, Rui M., Arnaldo J. Abrantes, and Jorge S. Marques. "Tracking the Human Body Using Multiple Predictors." In Articulated Motion and Deformable Objects, 155–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36138-3_13.

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Shen, Shuhan, and Weirong Chen. "Probability Evolutionary Algorithm Based Human Body Tracking." In Lecture Notes in Computer Science, 525–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11732242_50.

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9

Zeng, Chengbin, Huadong Ma, Anlong Ming, and Xiaobo Zhang. "3D Human Body Tracking in Unconstrained Scenes." In Advances in Multimedia Information Processing - PCM 2009, 119–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_10.

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Han, Tony X., and Thomas S. Huang. "Articulated Body Tracking Using Dynamic Belief Propagation." In Computer Vision in Human-Computer Interaction, 26–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573425_3.

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Тези доповідей конференцій з теми "TRACKING THE HUMAN BODY"

1

Demirdjian, Ko, and Darrell. "Constraining human body tracking." In ICCV 2003: 9th International Conference on Computer Vision. IEEE, 2003. http://dx.doi.org/10.1109/iccv.2003.1238468.

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2

Demirdjian, D. "Enforcing Constraints for Human Body Tracking." In 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10101.

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3

Xu, Yingkun, Lei Qin, Shuqiang Jiang, and Qingming Huang. "Human tracking by structured body parts." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116101.

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4

Huang, Chun-Hao, Edmond Boyer, and Slobodan Ilic. "Robust Human Body Shape and Pose Tracking." In 2013 International Conference on 3D Vision (3DV). IEEE, 2013. http://dx.doi.org/10.1109/3dv.2013.45.

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5

Gritai, A., and M. Shah. "Tracking of Human Body Joints using Anthropometry." In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262711.

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Valtonen, Miika, Henrik Raula, and Jukka Vanhala. "Human body tracking with electric field ranging." In the 14th International Academic MindTrek Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1930488.1930527.

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"HUMAN BODY TRACKING FOR PHYSIOTHERAPY VIRTUAL TRAINING." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0001364704490454.

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8

Yang, Jinfu, Jinrong Fu, and Mingai Li. "Robust human body tracking using sparse representation." In 2012 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2012. http://dx.doi.org/10.1109/icma.2012.6285718.

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Abdellaoui, Mehrez, Leila Kabbai, and Ali Douik. "New matching method for human body tracking." In 2014 11th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2014. http://dx.doi.org/10.1109/ssd.2014.6808838.

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Gu Junxia, Ding Xiaoqing, Wang Shengjin, and Wu Youshou. "Full body tracking-based human action recognition." In ICPR 2008 19th International Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761198.

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Звіти організацій з теми "TRACKING THE HUMAN BODY"

1

Wray, W. O., and T. Aida. Deformable human body model development. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/672307.

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Eveland, Christopher K., Diego A. Socolinsky, and Lawrence B. Wolff. Tracking Human Faces in Infrared Video. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444364.

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3

Yang, Jie, and Alex Waibel. Tracking Human Faces in Real-Time,. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada303256.

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Cottle, Frederick S., Pamela V. Ulrich, and Karla P. Simmons. Human Body Form: What Does It Mean? Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-450.

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Flanders, Benjamin J. An Alternative Representation of a Simulated Human Body. Fort Belvoir, VA: Defense Technical Information Center, November 2013. http://dx.doi.org/10.21236/ada591351.

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Matsumoto, David, Hyisung C. Hwang, Adam M. Fullenkamp, and C. M. Laurent. Human Deception Detection from Whole Body Motion Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ada626755.

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Balcells Capellades, Marti. An Appearance Based Approach for Human and Object Tracking. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada459305.

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Park, Jinhee, and Yun-Ja Nam. Development of Bodice Basic Pattern Algorithm Using 3D Human Body Shape Body Surface Pattern Flattening. Ames: Iowa State University, Digital Repository, November 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1710.

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MacLeod, Tynan, Timothy P. Rioux, Miyo Yokota, Peng Li, Brian D. Corner, and Xiaojiang Xu. Individualized Human CAD Models: Anthropmetric Morphing and Body Tissue Layering. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada609587.

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Dogaru, Traian, and Calvin Le. Validation of Xpatch Computer Models for Human Body Radar Signature. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada478870.

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