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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Current Result Document : 8 / 10 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) »ç°¢Áö¿ª°æº¸½Ã½ºÅÛÀ» À§ÇÑ ½Ç½Ã°£ ÃøÈĹæ Â÷·®°ËÃâ ¾Ë°í¸®Áò
¿µ¹®Á¦¸ñ(English Title) Real-Time Side-Rear Vehicle Detection Algorithm for Blind Spot Warning Systems
ÀúÀÚ(Author) À±±¤¿í   ÃÖ¼®È¯   ¾È»ó¾ð   ±èÁ¤±¸   ÃÖÀ±È£   Kwang-Wook Yun   Suck-Hwan Choi   Sang-Un An   Jeong-Goo Kim   Yoon-Ho Choi   °­Çö¿ì   ¹éÀå¿î   ÇѺ´±æ   Á¤À±¼ö   Hyunwoo Kang   Jang Woon Baek   Byung-Gil Han   Yoonsu Chung  
¿ø¹®¼ö·Ïó(Citation) VOL 23 NO. 07 PP. 0408 ~ 0416 (2017. 07)
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(Korean Abstract)
º» ³í¹®¿¡¼­´Â ÁÖÇà Áß »ç°¢Áö¿ª³»ÀÇ Â÷·®À» ºü¸£°í Á¤È®ÇÏ°Ô ½Ç½Ã°£À¸·Î °ËÃâÇÏ´Â ÃøÈĹæ Â÷·®°ËÃâ ¾Ë°í¸®ÁòÀ» Á¦¾ÈÇÑ´Ù. Á¦¾È ¾Ë°í¸®ÁòÀº ½Ç½Ã°£ 󸮸¦ À§ÇØ MCT(Modified Census Transformation) Ư¡º¤Å͸¦ ±â¹ÝÀ¸·Î ¿¡À̴ٺνºÆ® ÇнÀÀ» ÅëÇØ »ý¼ºµÇ´Â ij½ºÄÉÀÌµå ºÐ·ù±â¸¦ »ç¿ëÇÑ´Ù. MCT ºÐ·ù±â´Â °ËÃâÀ©µµ¿ì°¡ ÀÛÀ»¼ö·Ï 󸮼ӵµ°¡ ºü¸£°í, °ËÃâÀ©µµ¿ì°¡ Ŭ¼ö·Ï Á¤È®µµ°¡ Áõ°¡ÇÑ´Ù. Á¦¾È ¾Ë°í¸®ÁòÀº ÀÌ·¯ÇÑ Æ¯Â¡À» ÀÌ¿ëÇÏ¿© °ËÃâÀ©µµ¿ì°¡ ÀÛÀº ºÐ·ù±â·Î Â÷·®È常¦ ºü¸£°Ô »ý¼ºÇÑ ÈÄ º¸´Ù Å« »çÀÌÁîÀÇ °ËÃâ À©µµ¿ì¸¦ °¡Áö´Â ºÐ·ù±â·Î »ý¼ºµÈ Â÷·®Èĺ¸¿¡ ´ëÇØ Á¤È®ÇÏ°Ô Â÷·®ÀÎÁö °ËÁõÇÑ´Ù. ¶ÇÇÑ, Â÷·®ºÐ·ù±â¿Í ¹ÙÄû ºÐ·ù±â¸¦ µ¿½Ã¿¡ »ç¿ëÇÏ¿© »ç°¢Áö¿ª³»·Î ÁøÀÔÇÏ´Â Â÷·®°ú »ç°¢Áö¿ª³»ÀÇ ÀÎÁ¢Â÷·®À» È¿°úÀûÀ¸·Î °ËÃâÇÑ´Ù.
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(English Abstract)
This paper proposes a real-time side-rear vehicle detection algorithm that detects vehicles quickly and accurately in blind spot areas when driving. The proposed algorithm uses a cascade classifier created by AdaBoost Learning using the MCT (modified census transformation) feature vector. Using this classifier, the smaller the detection window, the faster the processing speed of the MCT classifier, and the larger the detection window, the greater the accuracy of the MCT classifier. By considering these characteristics, the proposed algorithm uses two classifiers with different detection window sizes . The first classifier quickly generates candidates with a small detection window. The second classifier accurately verifies the generated candidates with a large detection window. Furthermore, the vehicle classifier and the wheel classifier are simultaneously used to effectively detect a vehicle entering the blind spot area, along with an adjacent vehicle in the blind spot area.
Å°¿öµå(Keyword) ¹«¼± ³×Æ®¿öÅ©   WIPS   ħÀÔŽÁö   ħÀÔÂ÷´Ü   wireless network   WIPS   intrusion detection   intrusion protection   Â÷·®°ËÃâ   »ç°¢Áö¿ª°æº¸   ij½ºÄÉÀÌµå ºÐ·ù±â   ¿¡À̴ٺνºÆ®   MCT(modified census transformation)   vehicle detection   blind spot   cascade classifier   AdaBoost   modified census transform  
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