Hand Detection Using Cascade of Softmax Classifiers
Sliding-window based multiclass hand posture detections are often performed by detecting postures of each predefined category using an independent detector, which makes it lack efficiency and results in high postures confusion rates in real-time applications. To tackle such problems, in this work, a...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/9204854 |
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| author | Yan-Guo Zhao Feng Zheng Zhan Song |
| author_facet | Yan-Guo Zhao Feng Zheng Zhan Song |
| author_sort | Yan-Guo Zhao |
| collection | DOAJ |
| description | Sliding-window based multiclass hand posture detections are often performed by detecting postures of each predefined category using an independent detector, which makes it lack efficiency and results in high postures confusion rates in real-time applications. To tackle such problems, in this work, an efficient cascade detector that integrates multiple softmax-based binary (SftB) models and a softmax-based multiclass (SftM) model is investigated to perform multiclass posture detection in parallel. The SftB models are used to distinguish the predefined postures from the background regions, and the SftM model is applied to discriminate among all the predefined hand posture categories. Another usage of the cascade structure is that it could effectively decompose the complexity of background pattern space and therefore improve the detection accuracy. In addition, to balance the detection accuracy and efficiency, the HOG features of increasing resolutions will be adopted by classifiers of increasing stage-levels in the cascade structure. The experiments are implemented under various scenarios with complicated background and challenging lightings. Results show the superiority of the proposed SftB classifiers over the traditional binary classifiers such as logistic regression, as well as the accuracy and efficiency improvements brought by the softmax-based cascade architecture compared with the noncascade multiclass softmax detectors. |
| format | Article |
| id | doaj-art-a47b7da09a214453bca989bcc78e46cb |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-a47b7da09a214453bca989bcc78e46cb2025-02-03T01:12:31ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/92048549204854Hand Detection Using Cascade of Softmax ClassifiersYan-Guo Zhao0Feng Zheng1Zhan Song2Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSwanson School of Engineering, The University of Pittsburgh, Pittsburgh, PA 15261, USAShenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSliding-window based multiclass hand posture detections are often performed by detecting postures of each predefined category using an independent detector, which makes it lack efficiency and results in high postures confusion rates in real-time applications. To tackle such problems, in this work, an efficient cascade detector that integrates multiple softmax-based binary (SftB) models and a softmax-based multiclass (SftM) model is investigated to perform multiclass posture detection in parallel. The SftB models are used to distinguish the predefined postures from the background regions, and the SftM model is applied to discriminate among all the predefined hand posture categories. Another usage of the cascade structure is that it could effectively decompose the complexity of background pattern space and therefore improve the detection accuracy. In addition, to balance the detection accuracy and efficiency, the HOG features of increasing resolutions will be adopted by classifiers of increasing stage-levels in the cascade structure. The experiments are implemented under various scenarios with complicated background and challenging lightings. Results show the superiority of the proposed SftB classifiers over the traditional binary classifiers such as logistic regression, as well as the accuracy and efficiency improvements brought by the softmax-based cascade architecture compared with the noncascade multiclass softmax detectors.http://dx.doi.org/10.1155/2018/9204854 |
| spellingShingle | Yan-Guo Zhao Feng Zheng Zhan Song Hand Detection Using Cascade of Softmax Classifiers Advances in Multimedia |
| title | Hand Detection Using Cascade of Softmax Classifiers |
| title_full | Hand Detection Using Cascade of Softmax Classifiers |
| title_fullStr | Hand Detection Using Cascade of Softmax Classifiers |
| title_full_unstemmed | Hand Detection Using Cascade of Softmax Classifiers |
| title_short | Hand Detection Using Cascade of Softmax Classifiers |
| title_sort | hand detection using cascade of softmax classifiers |
| url | http://dx.doi.org/10.1155/2018/9204854 |
| work_keys_str_mv | AT yanguozhao handdetectionusingcascadeofsoftmaxclassifiers AT fengzheng handdetectionusingcascadeofsoftmaxclassifiers AT zhansong handdetectionusingcascadeofsoftmaxclassifiers |