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Overlay projection of 3D gait data on calibrated 2D video
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Oral Presentations / Gait & Posture 24S (2006) S7–S97
References [1] Schwartz MH, Sohrweide S. Proceedings of 13th annual ESMAC. 2004. [2] Rozumalski A, Schwartz MH. Proceedings of the 9th annual GCMAS. 2004. [3] Schwartz MH, Rozumalski A. J Biomech 2005;38/1:107–16.
doi:10.1016/j.gaitpost.2006.11.022 O-15 Overlay projection of 3D gait data on calibrated 2D video Tim V. Wrigley a,b a
Centre for Health, Exercise and Sports Medicine (CHESM), University of Melbourne, Australia b NH & MRC CCRE in Clinical Gait Analysis and Gait Rehabilitation, Melbourne, Australia
1. Summary/conclusions A straightforward technique for the calibration of an arbitrarily placed video camera in the same laboratory coordinate system as motion capture cameras and force plates is described. Captured 3D data and derived data can then be projected onto recorded 2D video. 2. Introduction Systems for video overlay of graphical analog data (e.g. EMG) or ground reaction force vectors (planar projections) have been developed in the past. However, despite the potential power of overlaying 3D information on video, projection of 3D data on 2D video recorded by an arbitrarily placed video camera has not been readily available. The straightforward calibration of a video camera in the same laboratory coordinate system as motion capture cameras and force plates in Matlab (Mathworks Inc., Massachusetts, USA) is described here. Captured 3D data is then projected onto recorded 2D video.
Fig. 1. Unibrain Fire-i camera.
(1280 × 940 at 120 frames/s) are used for motion capture of the reflective markers. A fixed ‘frame offset’ is empirically determined to time-match the 30 fps video frames to the 120 fps motion capture data. To calibrate the Firewire video camera, multiple still images of a planar, ‘checker-board’ are separately captured in different orientations (Fig. 2). The only information required about the planar board is the standard size of the squares. A second planar object is subsequently placed over one of the force plates (Fig. 3) to establish the video camera position and orientation in relation to the laboratory coordinate system for motion capture. The still images are processed in the public domain Camera Calibration Toolbox for Matlab developed by Bouguet [1], based on methods exploiting the unique characteristics of planar calibration objects [2,3]. The intrinsic camera parameters (focal length, principal point, radial and tangential distortion) are initially determined from the multiple board views, followed by the extrinsic parameters (position and rotation matrix) from the board over the force platform. No information about the video camera characteristics needs to be supplied for its calibration. The intrinsic and extrinsic param-
3. Statement of clinical significance Overlay of 3D data on 2D video has many potential applications in clinical gait analysis for visualization/quality control of 3D reconstruction, labelling, virtual markers (e.g. joint centres), and muscle/joint modelling.
4. Methods Video of gait trials is captured via Firewire to AVI file by Vicon Workstation (Vicon Peak, Oxford, UK) using a Fire-i camera (Unibrain SA, Athens, Greece), with 640 × 480 pixel resolution at 30 frames/s (Fig. 1). Eight Vicon M2 cameras
Fig. 2. Planar calibration board.
Oral Presentations / Gait & Posture 24S (2006) S7–S97
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5. Results and discussion Fig. 4 displays an example of the capabilities of the method showing, for simplicity, a single frame from the video with projections of the lateral knee marker trajectory and force plate locations. The presence of visible markers in the video sequence for which the 3D coordinates have been reconstructed provides a convenient in-built check that the projection is fundamentally accurate. In the case where a projected 3D coordinate of a given marker deviates slightly from the visual image of that reflective marker, it will be difficult to determine whether the small error lies in the reconstructed 3D coordinate or in the video camera calibration. However, more significant errors in 3D reconstruction, labelling, and virtual markers (e.g. joint centres) will be readily apparent, and much more obviously so than in standard stick figure or rendered skeleton representations without video overlay. Correct location and scaling of muscle and joint models will also be evident. Fig. 3. Extrinsic parameter determination.
References eters of the Vicon M2 motion capture cameras are determined via the standard Vicon Workstation ‘DynaCal’ camera calibration routines, using a 238 mm two-marker wand. In this example, the standard Vicon Plug-in-Gait lower limb marker set was used for the gait trial. After marker 3D reconstruction in Workstation, 3D data is retrieved from the recorded C3D file in Matlab using C3D Server (Motion Lab Systems, LA, USA). Standard Matlab routines are used to read the video AVI frames in sequence, and a routine in the Camera Calibration Toolbox performs a projection of the time-matched 3D data onto the 2D video frames.
[1] Bouguet. Camera calibration toolbox for Matlab. Latest version August 10 2005. http://www.vision.caltech.edu/bouguetj/calib doc/. [2] Zhang. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 2000;22(11):1330–4. [3] Heikkila, Silven. A four-step camera calibration procedure with implicit image correction. Proc IEEE CVPR97 1997:1106–12.
doi:10.1016/j.gaitpost.2006.11.023 O-16 Relationship between the anthropometric variables and frontal knee moments in healthy obese adults Priyanka Khole a,∗ , Neil Segal b , H. John Yack a a
Graduate Program in Physical Therapy and Rehabilitation Sciences, University of Iowa, IA, USA b Department of Orthopedics and Rehabilitation, University of Iowa, IA, USA
1. Summary/conclusions Increased body weight has been proposed as a risk factor for the development of knee OA. The relationship between body mass and the frontal plane knee moment, however, is only moderate. The knee moment is also influenced by individual anthropometric characteristics that help to define the distribution of mass.
2. Introduction
Fig. 4. Projection of 3D data onto video.
Osteoarthritis (OA) is a leading cause of disability among US adults causing pain, joint stiffness and limited mobility
Oral Presentations / Gait & Posture 24S (2006) S7–S97
References [1] Schwartz MH, Sohrweide S. Proceedings of 13th annual ESMAC. 2004. [2] Rozumalski A, Schwartz MH. Proceedings of the 9th annual GCMAS. 2004. [3] Schwartz MH, Rozumalski A. J Biomech 2005;38/1:107–16.
doi:10.1016/j.gaitpost.2006.11.022 O-15 Overlay projection of 3D gait data on calibrated 2D video Tim V. Wrigley a,b a
Centre for Health, Exercise and Sports Medicine (CHESM), University of Melbourne, Australia b NH & MRC CCRE in Clinical Gait Analysis and Gait Rehabilitation, Melbourne, Australia
1. Summary/conclusions A straightforward technique for the calibration of an arbitrarily placed video camera in the same laboratory coordinate system as motion capture cameras and force plates is described. Captured 3D data and derived data can then be projected onto recorded 2D video. 2. Introduction Systems for video overlay of graphical analog data (e.g. EMG) or ground reaction force vectors (planar projections) have been developed in the past. However, despite the potential power of overlaying 3D information on video, projection of 3D data on 2D video recorded by an arbitrarily placed video camera has not been readily available. The straightforward calibration of a video camera in the same laboratory coordinate system as motion capture cameras and force plates in Matlab (Mathworks Inc., Massachusetts, USA) is described here. Captured 3D data is then projected onto recorded 2D video.
Fig. 1. Unibrain Fire-i camera.
(1280 × 940 at 120 frames/s) are used for motion capture of the reflective markers. A fixed ‘frame offset’ is empirically determined to time-match the 30 fps video frames to the 120 fps motion capture data. To calibrate the Firewire video camera, multiple still images of a planar, ‘checker-board’ are separately captured in different orientations (Fig. 2). The only information required about the planar board is the standard size of the squares. A second planar object is subsequently placed over one of the force plates (Fig. 3) to establish the video camera position and orientation in relation to the laboratory coordinate system for motion capture. The still images are processed in the public domain Camera Calibration Toolbox for Matlab developed by Bouguet [1], based on methods exploiting the unique characteristics of planar calibration objects [2,3]. The intrinsic camera parameters (focal length, principal point, radial and tangential distortion) are initially determined from the multiple board views, followed by the extrinsic parameters (position and rotation matrix) from the board over the force platform. No information about the video camera characteristics needs to be supplied for its calibration. The intrinsic and extrinsic param-
3. Statement of clinical significance Overlay of 3D data on 2D video has many potential applications in clinical gait analysis for visualization/quality control of 3D reconstruction, labelling, virtual markers (e.g. joint centres), and muscle/joint modelling.
4. Methods Video of gait trials is captured via Firewire to AVI file by Vicon Workstation (Vicon Peak, Oxford, UK) using a Fire-i camera (Unibrain SA, Athens, Greece), with 640 × 480 pixel resolution at 30 frames/s (Fig. 1). Eight Vicon M2 cameras
Fig. 2. Planar calibration board.
Oral Presentations / Gait & Posture 24S (2006) S7–S97
S29
5. Results and discussion Fig. 4 displays an example of the capabilities of the method showing, for simplicity, a single frame from the video with projections of the lateral knee marker trajectory and force plate locations. The presence of visible markers in the video sequence for which the 3D coordinates have been reconstructed provides a convenient in-built check that the projection is fundamentally accurate. In the case where a projected 3D coordinate of a given marker deviates slightly from the visual image of that reflective marker, it will be difficult to determine whether the small error lies in the reconstructed 3D coordinate or in the video camera calibration. However, more significant errors in 3D reconstruction, labelling, and virtual markers (e.g. joint centres) will be readily apparent, and much more obviously so than in standard stick figure or rendered skeleton representations without video overlay. Correct location and scaling of muscle and joint models will also be evident. Fig. 3. Extrinsic parameter determination.
References eters of the Vicon M2 motion capture cameras are determined via the standard Vicon Workstation ‘DynaCal’ camera calibration routines, using a 238 mm two-marker wand. In this example, the standard Vicon Plug-in-Gait lower limb marker set was used for the gait trial. After marker 3D reconstruction in Workstation, 3D data is retrieved from the recorded C3D file in Matlab using C3D Server (Motion Lab Systems, LA, USA). Standard Matlab routines are used to read the video AVI frames in sequence, and a routine in the Camera Calibration Toolbox performs a projection of the time-matched 3D data onto the 2D video frames.
[1] Bouguet. Camera calibration toolbox for Matlab. Latest version August 10 2005. http://www.vision.caltech.edu/bouguetj/calib doc/. [2] Zhang. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 2000;22(11):1330–4. [3] Heikkila, Silven. A four-step camera calibration procedure with implicit image correction. Proc IEEE CVPR97 1997:1106–12.
doi:10.1016/j.gaitpost.2006.11.023 O-16 Relationship between the anthropometric variables and frontal knee moments in healthy obese adults Priyanka Khole a,∗ , Neil Segal b , H. John Yack a a
Graduate Program in Physical Therapy and Rehabilitation Sciences, University of Iowa, IA, USA b Department of Orthopedics and Rehabilitation, University of Iowa, IA, USA
1. Summary/conclusions Increased body weight has been proposed as a risk factor for the development of knee OA. The relationship between body mass and the frontal plane knee moment, however, is only moderate. The knee moment is also influenced by individual anthropometric characteristics that help to define the distribution of mass.
2. Introduction
Fig. 4. Projection of 3D data onto video.
Osteoarthritis (OA) is a leading cause of disability among US adults causing pain, joint stiffness and limited mobility