T. B. Moeslund and E. Granum, "A survey of computer vision-based human motion capture," Comput. Vis. Image Understand., vol. 81, pp. 231--268, 2001. Home/Search Document Details and Download
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Markerless Kinematic Model and Motion Capture from Volume Sequences - Jenkins (2003) (Correct)
....perform model and motion capture. In this paper, we introduce a solution for model free vision based markerless motion capture of subjects with tree structured kinematics from multiple calibrated cameras. Using the functional structure of a motion capture system described by Moeslund and Granum [11], we overview our approach for markerless motion capture. Moeslund and Granum describe a motion capture system as consisting of four components: initialization, tracking, pose estimation, and recognition. For initialization, a set of cameras are calibrated, using a method such as Bouguet s [1] ....
T. Moeslund and E. Granum. A survey of computer visionbased human motion capture. Computer Vision and Image Understanding, 81(3):231--268, March 2001.
Robust 3D Head Pose Classification Using Wavelets - Motwani (2003) (Correct)
....By recognizing the presence of these constraints it is possible to recognize the motion and pose of the head. We investigate the methods used for human motion capture to influence the design of our algorithm. Recently, comprehensive surveys of vision based human motion capture have been published [13, 24]. The method suggested by Campbell and Bobick [7] is impressive. They developed techniques for representation of body movements based on space curves in subspaces of a phase space. Phase space is a Euclidean space with axes for each of the independent variables of a system and their time ....
T. Moeslund and E. Granum. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding, 2001.
Human Body Model Acquisition and Tracking Using Voxel Data - Mikic, Trivedi, Hunter.. (2003) (5 citations) (Correct)
....called motion capture or posture estimation, is a problem of estimating the parameters of the human body model (such as joint angles) from the video data as the position and configuration of the tracked body change over time. A reliable motion capture system would be valuable in many applications [1, 2]. One class of applications are those where the extracted body model parameters are used directly, for example to interact with a virtual world, drive an animated avatar in a video game or for computer graphics character animation. Another class of applications use extracted parameters to classify ....
T. Moeslund, E. Granum, "A Survey of Computer Vision-Based Human Motion Capture", Computer Vision and Image Understanding 81,2001, p. 231-268
Autonomous Visual Events Detection and Classification.. - Xiang, Gong, Parkinson (Correct)
....events, we imply that both the number of meaningful events and their whereabout in the scene are automatically learned and detected rather than manually labelled or hypothesised as mostly reported in the literature. Numerous efforts have been made to model moving object behaviours in general [1, 7]. Most previous approaches for modelling behaviour heavily relied upon segmentation and tracking of object in the scene [5, 6, 11, 12] A visual event is commonly defined based on a moving object with constraints on its size, colour or shape. A sequence of events is represented by the tracked ....
T. Moeslund and E. Granum. A survey of computer vision-based human motion capture. CVIU, 81(3):231--268, 2001.
Robust Tracking And Segmentation Of Human Motion In An.. - Gonzalez, Lim, Fua.. (2003) (1 citation) (Correct)
....we demonstrate motion segmentation of different body parts in an image sequence. 1. INTRODUCTION Human body tracking in an image sequence gets much of attention, recently. Its range of applications is very wide and covers many areas such as visual surveillance, sports, entertainment and medicine[1]. In recent years, several frameworks have been proposed to address human body tracking and or modelling, either from multi view [2] 3] 4] 5] or single view sequences [6] 7] 8] Body tracking from a monocular sequence is an extremely challenging task, due to the high number of degrees of freedom ....
Thomas B. Moeslund and Erik Granum, "A survey of computer vision-based human motion capture," Computer Vision and Image Understanding: CVIU, vol. 81, no. 3, pp. 231--268, 2001.
3D Reconstruction of Sports Events for Digital TV - Malerczyk, Klein, Wiebesiek (2003) (Correct)
....work towards the 3d reconstruction of sports events. 2. RELATED WORK The analysis and tracking of human motion has been an active area of computer vision research during the last years. Detailed surveys of several 2d and 3d computer vision based techniques and their applications can be found in [Moeslund 01] and [Gavrila 99] Here only a few examples of markerless motion capturing applications shall be stated, which are most closely related to our approach. Chu et al. [Chu 03] aim at reconstructing human motion by capturing human volumes from multiple images without any underlying model. This ....
T. Moeslund and E. Granum, A Survey of Computer Vision-Based Human Motion Capture, Computer Vision and Image Understanding no. 81, pp.231-268, 2001
Speech-Gesture Driven Multimodal Interfaces for.. - Sharma, Yeasin.. (Correct)
....parts over time to understand the gesture. 2.4.1. Visual Tracking Visual tracking is one of the most actively researched fields in computer vision. A thorough discussion of human motion tracking methods is not possible here and the reader is hence referred to a number of reviews on this subject [48, 49]. Rather, we will discuss in this section, the challenges that vision based tracking algorithms encounter in the context of multimodal systems and to what degree standard approaches are suitable for different application domains. For multimodal HCI systems, a visual tracking algorithm has to ....
T. B. Moeslund and E. Granum, "A Survey of Computer Vision-Based Human Motion Capture," Computer Vision and Image Understanding, vol. 81, pp. 231-268, 2001.
Automatic Acquisition and Initialization of Kinematic Models - Krahnstoever, Yeasim.. (2001) (1 citation) (Correct)
....of attention from the computer vision research community. Many systems have shown to be able to perform various tracking, analysis and recognition tasks based on silhouette information [1] blobs [2] statistical (e.g. PCA or HMM) modeling [3] or explicit use of kinematic models [4, 5] See [6] and references therein for a recent survey on human motion analysis. Explicit body models are very promising because they directly encode the available domain knowledge and potentially offer a wider degree of generality and task independence than other approaches. However, remaining challenges ....
T. B. Moeslund and E. Granum, A survey of computer visionbased human motion capture, Computer Vision and Image Understanding, , no. 81, pp. 23168, 2001.
Automatic Acquisition and Initialization of Articulated.. - Krahnstoever, Yeasin.. (2002) (1 citation) (Correct)
....by O Rourke and Badler [23] In the context of human (or general articulated) motion tracking, the target is modeled 5 as a collection of segments connected by joints or springs. The number of links and joints and associated parameters used for articulated models vary widely across the literature [24]. Ju et al. 18] approximate humans with cardboard models, which are basically 2D models specialized at modeling humans seen from the side or front. Each segment of the model is described by a planar patch that can undergo planar projective motion. The motion of the patches is determined through ....
T. B. Moeslund and E. Granum, A survey of computer vision-based human motion capture, Computer Vision and Image Understanding, , no. 81, pp. 231--268, 2001.
Pose Estimation by Applied Numerical Techniques - Vittrup, Sørensen, McCane (2002) (1 citation) (Correct)
....furthermore makes use of stereoscopic vision. Keywords Numerical Optimisation, Pose Estimation, Analysis by Synthesis, Optimisation Algorithms, Downhill Simplex, Simulated Annealing, Spider I. Introduction H AND pose estimation and gesture recognition has been subject to intense research ([1]) as a means of next generation Human Computer Interaction (HCI) Traditionally, researchers have split approaches into an appearance based approach and a model based approach as done by Pavlovic et al. 2] The appearance based approach requires a set of predefined gestures, from which some ....
T. Moeslund and E. Granum, "A survey of computer visionbased human motion capture," Computer Vision and Image Understanding: CVIU, vol. 81, no. 3, pp 231--268, 2001.
Motion Capture of Articulated Chains by Applying Auxiliary.. - Moeslund, Granum Self-citation (Moeslund Granum) (Correct)
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T.B. Moeslund and E. Granum. A Survey of Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding, 81(3), 2001.
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T.B. Moeslund and E. Granum, "A Survey of Computer Vision-Based Human Motion Capture," Computer Vision and Image Understanding, vol. 81, no. 3, 2001.
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T.B. Moeslund and E. Granum. A Survey of Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding, 81(3), 2001.
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T.B. Moeslund and E. Granum. A Survey of Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding, 81(3), 2001.
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T.B. Moeslund and E. Granum. A Survey of Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding, 81(3), 2001. 8
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T.B. Moeslund and E. Granum, "A Survey of Computer Vision-Based Human Motion Capture," Computer Vision and Image Understanding, vol. 81, no. 3, 2001.
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T. B. Moeslund and E. Granum, "A survey of computer vision-based human motion capture," Comput. Vis. Image Understand., vol. 81, pp. 231--268, 2001.
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T. Moeslund, and E. Granum, "A Survey of Computer VisionBased Human Motion Capture", Computer Vision and Image Understanding (81), No 3, pages 231-268, 2001.
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T. Moeslund and E. Granum. A survey of computer visionbased human motion capture. Computer Vision and Image Understanding, 81(3):231--268, March 2001.
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T. B. Moeslund and E. Granum. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding, 81(3):231--268, March 2001.
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