Data-Free Prior Model for Upper Body Pose Estimation and Tracking
Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the ima...
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| Veröffentlicht in: | IEEE transactions on image processing Jg. 22; H. 12; S. 4627 - 4639 |
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| Sprache: | Englisch |
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IEEE
01.12.2013
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
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| Abstract | Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the image data to achieve a more robust body pose tracking. Various body prior models have been proposed. Most of them are data-driven, typically learned from 3D motion capture data. In addition to being expensive and time-consuming to collect, these data-based prior models cannot generalize well to activities and subjects not present in the motion capture data. To alleviate this problem, we propose to learn the prior model from anatomic, biomechanics, and physical constraints, rather than from the motion capture data. For this, we propose methods that can effectively capture different types of constraints and systematically encode them into the prior model. Experiments on benchmark data sets show the proposed prior model, compared with data-based prior models, achieves comparable performance for body motions that are present in the training data. It, however, significantly outperforms the data-based prior models in generalization to different body motions and to different subjects. |
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| AbstractList | Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the image data to achieve a more robust body pose tracking. Various body prior models have been proposed. Most of them are data-driven, typically learned from 3D motion capture data. In addition to being expensive and time-consuming to collect, these data-based prior models cannot generalize well to activities and subjects not present in the motion capture data. To alleviate this problem, we propose to learn the prior model from anatomic, biomechanics, and physical constraints, rather than from the motion capture data. For this, we propose methods that can effectively capture different types of constraints and systematically encode them into the prior model. Experiments on benchmark data sets show the proposed prior model, compared with data-based prior models, achieves comparable performance for body motions that are present in the training data. It, however, significantly outperforms the data-based prior models in generalization to different body motions and to different subjects. Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the image data to achieve a more robust body pose tracking. Various body prior models have been proposed. Most of them are data-driven, typically learned from 3D motion capture data. In addition to being expensive and time-consuming to collect, these data-based prior models cannot generalize well to activities and subjects not present in the motion capture data. To alleviate this problem, we propose to learn the prior model from anatomic, biomechanics, and physical constraints, rather than from the motion capture data. For this, we propose methods that can effectively capture different types of constraints and systematically encode them into the prior model. Experiments on benchmark data sets show the proposed prior model, compared with data-based prior models, achieves comparable performance for body motions that are present in the training data. It, however, significantly outperforms the data-based prior models in generalization to different body motions and to different subjects.Video based human body pose estimation seeks to estimate the human body pose from an image or a video sequence, which captures a person exhibiting some activities. To handle noise and occlusion, a pose prior model is often constructed and is subsequently combined with the pose estimated from the image data to achieve a more robust body pose tracking. Various body prior models have been proposed. Most of them are data-driven, typically learned from 3D motion capture data. In addition to being expensive and time-consuming to collect, these data-based prior models cannot generalize well to activities and subjects not present in the motion capture data. To alleviate this problem, we propose to learn the prior model from anatomic, biomechanics, and physical constraints, rather than from the motion capture data. For this, we propose methods that can effectively capture different types of constraints and systematically encode them into the prior model. Experiments on benchmark data sets show the proposed prior model, compared with data-based prior models, achieves comparable performance for body motions that are present in the training data. It, however, significantly outperforms the data-based prior models in generalization to different body motions and to different subjects. |
| Author | Qiang Ji Siqi Nie Jixu Chen |
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| Cites_doi | 10.1109/CVPR.2010.5540157 10.1145/378456.378507 10.1007/s11263-009-0273-6 10.1145/1390156.1390292 10.1109/CVPR.2008.4587360 10.1109/ICCV.2005.193 10.1007/s11263-009-0283-4 10.1109/ICCV.2011.6126500 10.5244/C.21.73 10.1109/CVPR.2008.4587580 10.1109/CVPR.2010.5540140 10.1109/CVPRW.2003.10101 10.1109/CVPR.2008.4587546 10.1023/B:VISI.0000042934.15159.49 10.1109/AFGR.1998.670920 10.1109/TPAMI.2006.21 10.1109/ICCVW.2009.5457532 10.1109/ICCV.2003.1238424 10.1007/978-3-540-75703-0_11 10.1109/CVPR.2005.335 10.1109/CVPR.2003.1211504 10.1109/CVPR.2004.1315063 10.1177/0278364907087172 10.1109/CVPR.2005.229 10.1109/CVPR.2009.5206672 10.1109/ICCV.2007.4409044 10.1214/aos/1176344136 |
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| Keywords | Performance evaluation Target tracking knowledge-based model body pose model Video signal Knowledge base Video recording Learning Biomechanics Human body model Body pose estimation Image sequence Position measurement Signal processing Motion detection |
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| SubjectTerms | Applied sciences Benchmarking Biological system modeling Biomechanical Phenomena - physiology Body pose estimation body pose model Data models Databases, Factual Detection, estimation, filtering, equalization, prediction Exact sciences and technology Human body Humans Image processing Image Processing, Computer-Assisted - methods Information, signal and communications theory Knowledge management knowledge-based model Models, Biological Motion perception Motion pictures Pose estimation Posture - physiology Signal and communications theory Signal processing Signal, noise Small mammals Telecommunications and information theory Three dimensional Tracking Training data Transaction processing |
| Title | Data-Free Prior Model for Upper Body Pose Estimation and Tracking |
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