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ÇѱÛÁ¦¸ñ(Korean Title) |
OBPCA ±â¹ÝÀÇ ¼öÁ÷´Ü¸é ÀÌ¿ë Â÷·® ÃßÃâ ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Vehicle Detection Method Based on Object-Based Point Cloud Analysis Using Vertical Elevation Data |
ÀúÀÚ(Author) |
ÀüÁعü
ÀÌÈñÁø
¿À»óÀ±
À̹μö
Junbeom Jeon
Heezin Lee
Sangyoon Oh
Minsu Lee
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¿ø¹®¼ö·Ïó(Citation) |
VOL 05 NO. 08 PP. 0369 ~ 0376 (2016. 08) |
Çѱ۳»¿ë (Korean Abstract) |
Á¡ Ŭ¶ó¿ìµå·ÎºÎÅÍ Â÷·®À» ÃßÃâÇÏ´Â ´Ù¾çÇÑ ¹æ½Ä Áß OBPCA ¹æ½ÄÀº ¼¼±×¸ÕÆ® ´ÜÀ§ÀÇ Æò°¡-ºÐ·ù·Î Á¤È®µµ°¡ ³ô°í, ´Ü¼øÇÑ Á÷»ç°¢Çü Æò¸éµµ¿¡¼ Ư¼º °ªµéÀ» ÃßÃâÇϹǷΠºÐ·ù°¡ ºü¸£´Ù. ±×·¯³ª ÀÌ OBPCA ¹æ½ÄÀº Â÷·®°ú Å©±â°¡ ºñ½ÁÇÑ Á÷À°¸éü ¸ð¾çÀÇ ¹°Ã¼¸¦ Â÷·®°ú ±¸º°ÇÏÁö ¸øÇÏ´Â ¹®Á¦¸¦ °¡Áö¹Ç·Î À̸¦ ±Øº¹ÇÏ°í Â÷·® ÃßÃâÀÇ Á¤È®µµ¸¦ ³ôÀÌ´Â ¹æ¾È¿¡ ´ëÇÑ ¿¬±¸°¡ ÇÊ¿äÇÏ´Ù. º» ³í¹®¿¡¼´Â ÀÌ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇØ ¼öÆò ´Ü¸é°ú ÇÔ²² ¼öÁ÷ ´Ü¸éÀ» ÀÌ¿ëÇÏ´Â È®Àå OBPCA ¹æ½ÄÀ» Á¦¾ÈÇÑ´Ù. Á¦¾È ¹æ¹ýÀº ¼öÆò ´Ü¸éÀ» ÅëÇØ Â÷·® È常¦ 1Â÷·Î ¼±º°ÇÏ°í, °¢ Â÷·® Èĺ¸¿¡¼ °¡Àå Ư¡ÀûÀÎ ¼öÁ÷ ´Ü¸éÀ» ã¾Æ¼ ±× ´Ü¸éÀÇ Æ¯¼º °ªµéÀ» ÀÓ°è°ªµé°ú ºñ±³ÇÏ¿© Â÷·® ¿©ºÎ¸¦ ÆÇ´ÜÇÑ´Ù. ºñ±³½ÇÇè¿¡¼´Â º» Á¦¾È¹æ½ÄÀÌ ±âÁ¸ OBPCA ¹æ½Ä¿¡ ºñÇØ Á¤¹Ðµµ°¡ 6.61% Çâ»óµÇ°í À§¾ç¼º·üÀÌ 13.96% °¨¼ÒµÊÀ» È®ÀÎÇßÀ¸¸ç, À̸¦ ÅëÇØ Á¦¾È ¹æ½ÄÀÌ ±âÁ¸ OBPCA ºÐ·ù¿À·ù ¹®Á¦¿¡ ´ëÇØ È¿°úÀûÀÎ ÇØ°á¹æ¾ÈÀÓÀ» º¸¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Among various vehicle extraction techniques, OBPCA (Object-Based Point Cloud Analysis) calculates features quickly by coarse-grained rectangles from top-view of the vehicle candidates. However, it uses only a top-view rectangle to detect a vehicle. Thus, it is hard to extract rectangular objects with similar size. For this reason, accuracy issue has raised on the OBPCA method which influences on DEM generation and traffic monitoring tasks. In this paper, we propose a novel method which uses the most distinguishing vertical elevations to calculate additional features. Our proposed method uses same features with top-view, determines new thresholds, and decides whether the candidate is vehicle or not. We compared the accuracy and execution time between original OBPCA and the proposed one. The experiment result shows that our method produces 6.61% increase of precision and 13.96% decrease of false positive rate despite with marginal increase of execution time. We can see that the proposed method can reduce misclassification. |
Å°¿öµå(Keyword) |
¶óÀÌ´Ù µ¥ÀÌÅÍ
Â÷·® ÃßÃâ
OBPCA
¼öÁ÷ ´Ü¸é
LiDAR Data
Vehicle Detection
OBPCA
Vertical Elevation
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