Human Pose Recognition Based on Depth Image Multifeature Fusion
The recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and self-occlusion. To solve this problem, a fe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/6271348 |
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| author | Haikuan Wang Feixiang Zhou Wenju Zhou Ling Chen |
| author_facet | Haikuan Wang Feixiang Zhou Wenju Zhou Ling Chen |
| author_sort | Haikuan Wang |
| collection | DOAJ |
| description | The recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and self-occlusion. To solve this problem, a feature extraction method combining directional gradient of depth feature (DGoD) and local difference of depth feature (LDoD) is proposed in this paper, which uses a novel strategy that incorporates eight neighborhood points around a pixel for mutual comparison to calculate the difference between the pixels. A new data set is then established to train the random forest classifier, and a random forest two-way voting mechanism is adopted to classify the pixels on different parts of the human body depth image. Finally, the gravity center of each part is calculated and a reasonable point is selected as the joint to extract human skeleton. The experimental results show that the robustness and accuracy are significantly improved, associated with a competitive operating efficiency by evaluating our approach with the proposed data set. |
| format | Article |
| id | doaj-art-4aa11609956149658ef805850f46b3da |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-4aa11609956149658ef805850f46b3da2025-02-03T01:07:48ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/62713486271348Human Pose Recognition Based on Depth Image Multifeature FusionHaikuan Wang0Feixiang Zhou1Wenju Zhou2Ling Chen3School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 201900, ChinaSchool of Mechatronics Engineering and Automation, Shanghai University, Shanghai 201900, ChinaSchool of Mechatronics Engineering and Automation, Shanghai University, Shanghai 201900, ChinaSchool of Mechatronics Engineering and Automation, Shanghai University, Shanghai 201900, ChinaThe recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and self-occlusion. To solve this problem, a feature extraction method combining directional gradient of depth feature (DGoD) and local difference of depth feature (LDoD) is proposed in this paper, which uses a novel strategy that incorporates eight neighborhood points around a pixel for mutual comparison to calculate the difference between the pixels. A new data set is then established to train the random forest classifier, and a random forest two-way voting mechanism is adopted to classify the pixels on different parts of the human body depth image. Finally, the gravity center of each part is calculated and a reasonable point is selected as the joint to extract human skeleton. The experimental results show that the robustness and accuracy are significantly improved, associated with a competitive operating efficiency by evaluating our approach with the proposed data set.http://dx.doi.org/10.1155/2018/6271348 |
| spellingShingle | Haikuan Wang Feixiang Zhou Wenju Zhou Ling Chen Human Pose Recognition Based on Depth Image Multifeature Fusion Complexity |
| title | Human Pose Recognition Based on Depth Image Multifeature Fusion |
| title_full | Human Pose Recognition Based on Depth Image Multifeature Fusion |
| title_fullStr | Human Pose Recognition Based on Depth Image Multifeature Fusion |
| title_full_unstemmed | Human Pose Recognition Based on Depth Image Multifeature Fusion |
| title_short | Human Pose Recognition Based on Depth Image Multifeature Fusion |
| title_sort | human pose recognition based on depth image multifeature fusion |
| url | http://dx.doi.org/10.1155/2018/6271348 |
| work_keys_str_mv | AT haikuanwang humanposerecognitionbasedondepthimagemultifeaturefusion AT feixiangzhou humanposerecognitionbasedondepthimagemultifeaturefusion AT wenjuzhou humanposerecognitionbasedondepthimagemultifeaturefusion AT lingchen humanposerecognitionbasedondepthimagemultifeaturefusion |