Real-Time Depth-Based Hand Detection and Tracking
This paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learn...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/284827 |
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| _version_ | 1832563207940603904 |
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| author | Sung-Il Joo Sun-Hee Weon Hyung-Il Choi |
| author_facet | Sung-Il Joo Sun-Hee Weon Hyung-Il Choi |
| author_sort | Sung-Il Joo |
| collection | DOAJ |
| description | This paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learning. The features of each candidate segment are to be computed by subtracting the averages of depth values of subblocks from the central depth value of the segment. The features are selectively employed according to their discriminating power when constructing the classifier. To predict a hand region in a successive frame, a seed point in the next frame is to be determined. Starting from the seed point, a region growing scheme is applied to obtain a hand region. To determine the central point of a hand, we propose the so-called Depth Adaptive Mean Shift algorithm. DAM-Shift is a variant of CAM-Shift (Bradski, 1998), where the size of the search disk varies according to the depth of a hand. We have evaluated the proposed hand detection and tracking algorithm by comparing it against the existing AdaBoost (Friedman et al., 2000) qualitatively and quantitatively. We have analyzed the tracking accuracy through performance tests in various situations. |
| format | Article |
| id | doaj-art-2a42dd80a56e4aae9f164b526c3aee03 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2a42dd80a56e4aae9f164b526c3aee032025-02-03T01:20:43ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/284827284827Real-Time Depth-Based Hand Detection and TrackingSung-Il Joo0Sun-Hee Weon1Hyung-Il Choi2Department of Media, Soongsil University, Seoul 156-743, Republic of KoreaDepartment of Media, Soongsil University, Seoul 156-743, Republic of KoreaDepartment of Media, Soongsil University, Seoul 156-743, Republic of KoreaThis paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learning. The features of each candidate segment are to be computed by subtracting the averages of depth values of subblocks from the central depth value of the segment. The features are selectively employed according to their discriminating power when constructing the classifier. To predict a hand region in a successive frame, a seed point in the next frame is to be determined. Starting from the seed point, a region growing scheme is applied to obtain a hand region. To determine the central point of a hand, we propose the so-called Depth Adaptive Mean Shift algorithm. DAM-Shift is a variant of CAM-Shift (Bradski, 1998), where the size of the search disk varies according to the depth of a hand. We have evaluated the proposed hand detection and tracking algorithm by comparing it against the existing AdaBoost (Friedman et al., 2000) qualitatively and quantitatively. We have analyzed the tracking accuracy through performance tests in various situations.http://dx.doi.org/10.1155/2014/284827 |
| spellingShingle | Sung-Il Joo Sun-Hee Weon Hyung-Il Choi Real-Time Depth-Based Hand Detection and Tracking The Scientific World Journal |
| title | Real-Time Depth-Based Hand Detection and Tracking |
| title_full | Real-Time Depth-Based Hand Detection and Tracking |
| title_fullStr | Real-Time Depth-Based Hand Detection and Tracking |
| title_full_unstemmed | Real-Time Depth-Based Hand Detection and Tracking |
| title_short | Real-Time Depth-Based Hand Detection and Tracking |
| title_sort | real time depth based hand detection and tracking |
| url | http://dx.doi.org/10.1155/2014/284827 |
| work_keys_str_mv | AT sungiljoo realtimedepthbasedhanddetectionandtracking AT sunheeweon realtimedepthbasedhanddetectionandtracking AT hyungilchoi realtimedepthbasedhanddetectionandtracking |