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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
±íÀÌÁ¤º¸¸¦ ÀÌ¿ëÇÑ ½Ç½Ã°£ ¼Õ ¿µ¿ª °ËÃâ ¹× ÃßÀû |
¿µ¹®Á¦¸ñ(English Title) |
Real-time Hand Region Detection and Tracking using Depth Information |
ÀúÀÚ(Author) |
ÁÖ¼ºÀÏ
¿ø¼±Èñ
ÃÖÇüÀÏ
SungIl Joo
SunHee Weon
HyungIl Choi
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¿ø¹®¼ö·Ïó(Citation) |
VOL 01 NO. 03 PP. 0177 ~ 0186 (2012. 12) |
Çѱ۳»¿ë (Korean Abstract) |
º» ³í¹®¿¡¼´Â ½Ç½Ã°£ ¼Õµ¿ÀÛ ºÐ¼®À» À§ÇÑ ±íÀÌÁ¤º¸ ±â¹Ý ¼Õ ¿µ¿ª °ËÃâ ¹× ÃßÀû ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. À̸¦ À§ÇØ ¼Õ ¿µ¿ª °ËÃâ´Ü°è¿¡¼´Â ±íÀÌ Á¤º¸¸¸À» ÀÌ¿ëÇÏ¿© ¼Õ ¿µ¿ªÀÇ Æ¯Â¡ÀÎ ÇüŸðµ¨À» »ý¼ºÇÏ°í, °ËÃ⠽à ¿òÁ÷ÀÓ Á¤º¸¿Í ¿µ¿ª È®Àå(Region Growing)À» ÅëÇØ °´Ã¼¸¦ ÃßÃâÇÑ´Ù. ÃßÃâµÈ °´Ã¼´Â »çÀü¿¡ »ý¼ºµÈ ÇüŸ𵨰ú Å©±âÁ¤º¸¸¦ ºÐ¼®ÇÏ¿© ÃÖÁ¾ ¼Õ ¿µ¿ªÀ¸·Î ÆÇÁ¤ÇÑ´Ù. ÆÇÁ¤µÈ ¼Õ °´Ã¼´Â ÃßÀû´Ü°è¿¡¼ Áß½ÉÁ¡ ÀüÀÌ °úÁ¤À» ÅëÇØ ÀÌÀü Áß½ÉÁ¡°úÀÇ ÃÖ±ÙÁ¢Á¡À» ȹµæÇÏ°í, ÃÖ±ÙÁ¢Á¡À¸·ÎºÎÅÍ ¿µ¿ª È®Àå°ú ±íÀ̱â¹Ý ÀûÀÀÀû Æò±Õ À̵¿ ±â¹ý(DAM-Shift)À» ÅëÇØ »õ·Î¿î Áß½ÉÁ¡À» °ËÃâÇÏ¿© ÃßÀûÇÑ´Ù. ¸¶Áö¸·À¸·Î ¼º´É °ËÁõÀ» À§ÇØ ´Ù¾çÇÑ ¼Õ ¸ð¾ç°ú ¼Óµµ ¹× À§Ä¡¿¡ ´ëÇÑ ´Ù¾çÇÑ È¯°æ¿¡¼ ½ÇÇèÇÏ°í, °ËÃâ¼Óµµ¿Í ÃßÀûµÈ ±ËÀûÀÇ Á¤·®Àû, Á¤¼ºÀû ºÐ¼®À» ÅëÇØ Á¦¾ÈÇÏ´Â ¹æ¹ýÀÇ È¿À²¼ºÀ» ÀÔÁõÇÑ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.
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Å°¿öµå(Keyword) |
¼Õ ¿µ¿ª °ËÃâ
´©ÀûÂ÷¿µ»ó
±íÀÌ¿µ»ó
¼Õ ÃßÀû
Å°³ØÆ®
±íÀÌ¿µ»ó ±â¹Ý ÀûÀÀÀû Æò±Õ À̵¿
Hand Region Detection
Accumulated Difference Image
Depth Image
Hand Tracking
Kinect
DAM-Shift
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ÆÄÀÏ÷ºÎ |
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