TY - GEN
T1 - A two-layer night-time vehicle detector
AU - Wang, Weihong
AU - Shen, Chunhua
AU - Zhang, Jian
AU - Paisitkriangkrai, Sakrapee
PY - 2009
Y1 - 2009
N2 - We present a two-layer night time vehicle detector in this work. At the first layer, vehicle headlight detection [1, 2, 3] is applied to find areas (bounding boxes) where the possible pairs of headlights locate in the image, the Haar feature based AdaBoost framework is then applied to detect the vehicle front. This approach has achieved a very promising performance for vehicle detection at night time. Our results show that the proposed algorithm can obtain a detection rate of over 90% at a very low false positive rate (1:5%). Without any code optimization, it also performs at a faster speed compared to the standard Haar feature based AdaBoost approach [4].
AB - We present a two-layer night time vehicle detector in this work. At the first layer, vehicle headlight detection [1, 2, 3] is applied to find areas (bounding boxes) where the possible pairs of headlights locate in the image, the Haar feature based AdaBoost framework is then applied to detect the vehicle front. This approach has achieved a very promising performance for vehicle detection at night time. Our results show that the proposed algorithm can obtain a detection rate of over 90% at a very low false positive rate (1:5%). Without any code optimization, it also performs at a faster speed compared to the standard Haar feature based AdaBoost approach [4].
UR - http://www.scopus.com/inward/record.url?scp=77950301471&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2009.33
DO - 10.1109/DICTA.2009.33
M3 - Conference contribution
SN - 9780769538662
T3 - DICTA 2009 - Digital Image Computing: Techniques and Applications
SP - 162
EP - 167
BT - DICTA 2009 - Digital Image Computing
T2 - Digital Image Computing: Techniques and Applications, DICTA 2009
Y2 - 1 December 2009 through 3 December 2009
ER -