Mining Spatialoral Patterns and Structural Sparsity for Human Motion Data Denoising

Authors: Feng, Y., Ji, M., Xiao, J., Yang, X., Zhang, J.J., Zhuang, Y. and Li, X.

Journal: IEEE Transactions on Cybernetics

Volume: 45

Issue: 12

Pages: 2693-2706

ISSN: 2168-2267

DOI: 10.1109/TCYB.2014.2381659

Abstract:

Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatialoral patterns and the structural sparsity embedded in motion data. We first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.

https://eprints.bournemouth.ac.uk/23043/

Source: Scopus

Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising.

Authors: Feng, Y., Ji, M., Xiao, J., Yang, X., Zhang, J.J., Zhuang, Y. and Li, X.

Journal: IEEE Trans Cybern

Volume: 45

Issue: 12

Pages: 2693-2706

eISSN: 2168-2275

DOI: 10.1109/TCYB.2014.2381659

Abstract:

Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal patterns and the structural sparsity embedded in motion data. We first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.

https://eprints.bournemouth.ac.uk/23043/

Source: PubMed

Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising

Authors: Feng, Y., Ji, M., Xiao, J., Yang, X., Zhang, J.J., Zhuang, Y. and Li, X.

Journal: IEEE TRANSACTIONS ON CYBERNETICS

Volume: 45

Issue: 12

Pages: 2693-2706

eISSN: 2168-2275

ISSN: 2168-2267

DOI: 10.1109/TCYB.2014.2381659

https://eprints.bournemouth.ac.uk/23043/

Source: Web of Science (Lite)

Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising

Authors: Feng, Y., Ji, M., Xiao, J., Yang, X., Zhang, J., Zhuang, Y. and Li, X.

Journal: IEEE Transactions on Cybernetics

Volume: 45

Issue: 12

Pages: 2693-2706

ISSN: 2168-2267

DOI: 10.1109/TCYB.2014.2381659

Abstract:

Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal patterns and the structural sparsity embedded in motion data. We first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.

https://eprints.bournemouth.ac.uk/23043/

Source: Manual

Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising.

Authors: Feng, Y., Ji, M., Xiao, J., Yang, X., Zhang, J.J., Zhuang, Y. and Li, X.

Journal: IEEE transactions on cybernetics

Volume: 45

Issue: 12

Pages: 2693-2706

eISSN: 2168-2275

ISSN: 2168-2267

DOI: 10.1109/tcyb.2014.2381659

Abstract:

Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal patterns and the structural sparsity embedded in motion data. We first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.

https://eprints.bournemouth.ac.uk/23043/

Source: Europe PubMed Central

Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising

Authors: Feng, Y., Ji, M., Xiao, J., Yang, X., Zhang, J.J., Zhuang, Y. and Liu, X.

Journal: IEEE Transactions on Cybernetics

Volume: 45

Issue: 12

Pages: 2693-2706

ISSN: 2168-2267

Abstract:

Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal patterns and the structural sparsity embedded in motion data. We first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.

https://eprints.bournemouth.ac.uk/23043/

Source: BURO EPrints