TY - GEN
T1 - An adaptive selective background learning-hole filling algorithm to improve vehicle detection
AU - Alhamidi, Machmud R.
AU - Ayunina, Qurrotin
AU - Wibisono, Ari
AU - Mursanto, Petrus
AU - Jatmiko, Wisnu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/19
Y1 - 2016/2/19
N2 - Transportation plays an important role in urban development However, the vehicle growth in Indonesia is not supported by the number of road. Due to this fact, traffic congestion is easily occurred, especially in big cities. Intelligent Transportation System (ITS) has huge contribution to decrease the traffic congestion. In ITS, vehicle detection is one of challenging issue for traffic surveillance. In this paper, adaptive selective background learning and hole filling algorithm are applied to improve the vehicle detection. The validity of the proposed method is tested by using three scenarios and two parameters. The scenarios are bad weather close range (BW-CR), normal weather close range (NW-CR) and normal weather wide range (NW-WR). While, the parameters are the time duration of stopped vehicle detection and the pixel accuracy. Then, the proposed method (Adaptive Selective Background Learning-Hole Filling algorithm) is compared by another previous vehicle detection method. Generally, the result shows that the proposed method yields a significant improvement in vehicle detection. ASBL-HF can detect the stopped and moved vehicle with free noises. Moreover, ASBL-HF has the best accuracy. The accuracy value is about 98.2%.
AB - Transportation plays an important role in urban development However, the vehicle growth in Indonesia is not supported by the number of road. Due to this fact, traffic congestion is easily occurred, especially in big cities. Intelligent Transportation System (ITS) has huge contribution to decrease the traffic congestion. In ITS, vehicle detection is one of challenging issue for traffic surveillance. In this paper, adaptive selective background learning and hole filling algorithm are applied to improve the vehicle detection. The validity of the proposed method is tested by using three scenarios and two parameters. The scenarios are bad weather close range (BW-CR), normal weather close range (NW-CR) and normal weather wide range (NW-WR). While, the parameters are the time duration of stopped vehicle detection and the pixel accuracy. Then, the proposed method (Adaptive Selective Background Learning-Hole Filling algorithm) is compared by another previous vehicle detection method. Generally, the result shows that the proposed method yields a significant improvement in vehicle detection. ASBL-HF can detect the stopped and moved vehicle with free noises. Moreover, ASBL-HF has the best accuracy. The accuracy value is about 98.2%.
KW - Adaptive Selective Background Learning
KW - Intelligent Transportation System (ITS)
KW - Traffic Surveillance
KW - Vehicle Detection
UR - http://www.scopus.com/inward/record.url?scp=84964521208&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS.2015.7415188
DO - 10.1109/ICACSIS.2015.7415188
M3 - Conference contribution
AN - SCOPUS:84964521208
T3 - ICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings
SP - 237
EP - 242
BT - ICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Advanced Computer Science and Information Systems, ICACSIS 2015
Y2 - 10 October 2015 through 11 October 2015
ER -