TY - GEN
T1 - A real time vehicle counting based on adaptive tracking approach for highway videos
AU - Soleh, Muhamad
AU - Jati, Grafika
AU - Sasongko, Ananto Tri
AU - Jatmiko, Wisnu
AU - Hilman, M. H.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Detection, Tracking, and Counting the number of vehicles is the main foundation of intelligent systems for monitoring vehicle traffic flow. This research is going to perform vehicle detection using background subtraction algorithm and morphology operation. The result of those methods categorized as candidate object. Contour detection applied to define the object from its candidate. Vehicle is determined using threshold area of contour properties. The detected vehicles is tracked using an adaptive distance similarity measurement. Then, vehicle will be counted using the counting line after its vehicles pass that line. The proposed method is tested in four datasets with different challenges such as differences in light, weather, camera vibration, and image blurring. The research obtains satisfactory results especially in noon and rainy dataset with the accuracy higher than 93% for vehicle detection, tracking, and counting. The proposed method is able to detect, perform tracking, and counting the number of vehicles in a real time for highway videos.
AB - Detection, Tracking, and Counting the number of vehicles is the main foundation of intelligent systems for monitoring vehicle traffic flow. This research is going to perform vehicle detection using background subtraction algorithm and morphology operation. The result of those methods categorized as candidate object. Contour detection applied to define the object from its candidate. Vehicle is determined using threshold area of contour properties. The detected vehicles is tracked using an adaptive distance similarity measurement. Then, vehicle will be counted using the counting line after its vehicles pass that line. The proposed method is tested in four datasets with different challenges such as differences in light, weather, camera vibration, and image blurring. The research obtains satisfactory results especially in noon and rainy dataset with the accuracy higher than 93% for vehicle detection, tracking, and counting. The proposed method is able to detect, perform tracking, and counting the number of vehicles in a real time for highway videos.
KW - Adaptive Tracking
KW - Highway Videos
KW - Real Time
KW - Vehicle Counting
KW - Vehicle Detection
UR - http://www.scopus.com/inward/record.url?scp=85050761853&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2017.8275108
DO - 10.1109/IWBIS.2017.8275108
M3 - Conference contribution
AN - SCOPUS:85050761853
T3 - Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
SP - 93
EP - 98
BT - Proceedings - WBIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Workshop on Big Data and Information Security, WBIS 2017
Y2 - 23 September 2017 through 24 September 2017
ER -