Human motion reconstruction from inter-frame feature correspondences of a single video stream using a motion library

Park, Min Je; Choi, Min

doi:10.1145/545261.545280

Cited by 13 publications

(3 citation statements)

References 28 publications

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“…They utilize the neighborhood graph to find similar poses and motion interpolation for motion reconstruction. Park et al [12] describe a novel method for human motion reconstruction from the inter-frame feature correspondence in case of video streaming by employing some motion capture library. They reconstruct the human motion using time-warping, joint orientations and root trajectories.…”

Section: Related Workmentioning

confidence: 99%

Model based full body human motion reconstruction from video data

Yasin

Krüger

Weber

2013

Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Application

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This paper introduces a novel framework for full body human motion reconstruction from 2D video data using a motion capture database as knowledge base containing information on how people move. By extracting suitable twodimensional features from both, the input video sequence and the motion capture database, we are able to employ an efficient retrieval technique to run a data-driven optimization. Only little preprocessing is needed by our method, the reconstruction process runs close to real time. We evaluate the proposed techniques on synthetic two-dimensional input data obtained from motion capture data and on real video data.

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Section: Related Workmentioning

confidence: 99%

Model based full body human motion reconstruction from video data

Yasin

Krüger

Weber

2013

Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Application

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“…To increase the robustness, [7] uses semi-supervised learning. In [8] prior knowledge about the human movements is necessary, which can be stored in a motion library [9]. [10] maps typical postures into two-dimensional images.…”

Section: State Of the Artmentioning

confidence: 99%

A model-based algorithm to estimate body poses using stereo vision

Muhlbauer¹,

Kühnlenz²,

Buß³

2008

RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication

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Estimating the human body pose is of great interest for many tasks, such as human robot interaction, people tracking and surveillance. During the recent years, several approaches have been presented, which still have weaknesses regarding occlusions or complex scenes. In this paper, we present a novel algorithm for human body pose estimation using any three-dimensional representation of the environment, like stereo vision. The presented algorithm is able to leave out body parts and is therefore able to deal with occluded body parts. In a first step, possible humans need to be detected, e.g. by using a skin color filter. A disparity map containing depth information is computed using a stereo matching algorithm. It leads to a three-dimensional representation of the scene. Starting with the detected skin parts, our algorithm segments this point cloud into smaller clusters. The possible matches are then verified, and the body pose is estimated using a kinematic human model with 28 degrees of freedom. As our algorithm is capable of dealing with arbitrary three-dimensional representations, it can easily be adapted to use a three-dimensional laser range finder instead of a stereo camera system.

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“…At a different level, the idea of being able to recover the motion of actors or historical celebrities from old movies and bringing them to life in new movies, animations, games or virtual environments is also very attractive. To compensate the lack of calibration, manual specification of key features such as joints or adjustment of a reference skeleton to specific frames are crucial [14]. To facilitate the process, motion databases can be used, becoming indispensable the containment of similar motion clips to the motion being recovered [15,16].…”

Section: Introductionmentioning

confidence: 99%

3D Human Motion Reconstruction Using Video Processing

Roodsarabi

Behrad

2008

Lecture Notes in Computer Science

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Abstract.One of the important problems in human motion analysis is the 3D reconstruction of human motion, which utilizes the anatomic point's positions. These points can uniquely define the position and orientation of all anatomical segments. In this paper, a new method for reconstruction of human motion from the image sequence of a single static camera is described. In this method 2D tracking is used for 3D reconstruction, which a database of selected frames are used for the correction of tracking process. We use Discrete Cosine Transform (DCT) block as a "Matrix des criptor" used in the matching process for finding appropriate frame in the database and tracking process. Finally, 3D reconstruction is performed using Taylor method. By using DCT, we can select best frequency region for various tasks such as tracking, matching, correcting joints and so on. Experimental results showed the promise of the algorithm.

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Human motion reconstruction from inter-frame feature correspondences of a single video stream using a motion library

Cited by 13 publications

References 28 publications

Model based full body human motion reconstruction from video data

Model based full body human motion reconstruction from video data

A model-based algorithm to estimate body poses using stereo vision

3D Human Motion Reconstruction Using Video Processing

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