Markerless human body motion capture using Markov random field and dynamic graph cuts

Wan, Chengkai; Yuan, Baozong; Zhang, Miao

doi:10.1007/s00371-007-0195-7

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…[6]. Different from other algorithms in this field it takes the impact of errors in 3D data into account and integrates motion capture and 3D reconstruction into MRF-MAP framework and solves the two issues at the same time.…”

Section: Discussionmentioning

confidence: 98%

“…[6] works well in most of cases, but it is not robust in the case of body parts coming close to each other. The limbs may keep stuck to the wrong part of the data upon their actual departure.…”

Section: Incorporation Of Color Constraintsmentioning

confidence: 94%

“…We have demonstrated its efficiency in ref. [6]. This algorithm works well in most cases, but it is not robust against body part coming close to each other.…”

Section: Introductionmentioning

confidence: 95%

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Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

Wan

Zhang

et al. 2009

Sci. China Ser. F-Inf. Sci.

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Currently, many vision-based motion capture systems require passive markers attached to key locations on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on Markov random field (MRF) and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints into the pose estimation process. The advantages of incorporating color constraints are demonstrated by experimental results on several video sequences.human motion capture, Markov random field, dynamic graph-cut, color constrains

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Section: Discussionmentioning

confidence: 98%

Section: Incorporation Of Color Constraintsmentioning

confidence: 94%

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Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

Wan

Zhang

et al. 2009

Sci. China Ser. F-Inf. Sci.

Self Cite

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“…We record video from eleven AXIS IP cameras [5] that provide video as Motion JPEG via HTTP. The Motion JPEG is chosen since it is more suitable for seeking frames at different times, which is important for both online and off-line analysis.…”

Section: System Components • Video Recordingmentioning

confidence: 99%

“…Developing a real-time and fully automatic human motion capture system is a subject of great scientific and commercial interest [1][2][3][4][5][6][7][8], and it finds application in a variety of fields such as bio-mechanical analysis [1], humancomputer interfaces [2], film and game industries, and intelligent video surveillance.…”

Section: Introductionmentioning

confidence: 99%

A Multi-camera Network System for Markerless 3D Human Body Voxel Reconstruction

Yang

Zhang

et al. 2009

2009 Fifth International Conference on Image and Graphics

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Analyzing Gait Using a Time-of-Flight Camera

2009

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An algorithm is created, which performs human gait analysis using spatial data and amplitude images from a Time-of-flight camera. For each frame in a sequence the camera supplies cartesian coordinates in space for every pixel. By using an articulated model the subject pose is estimated in the depth map in each frame. The pose estimation is based on likelihood, contrast in the amplitude image, smoothness and a shape prior used to solve a Markov random field. Based on the pose estimates, and the prior that movement is locally smooth, a sequential model is created, and a gait analysis is done on this model. The output data are: Speed, Cadence (steps per minute), Step length, Stride length (stride being two consecutive steps also known as a gait cycle), and Range of motion (angles of joints). The created system produces good output data of the described output parameters and requires no user interaction.

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Markerless human body motion capture using Markov random field and dynamic graph cuts

Cited by 12 publications

References 15 publications

Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

A Multi-camera Network System for Markerless 3D Human Body Voxel Reconstruction

Analyzing Gait Using a Time-of-Flight Camera

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