TY - GEN
T1 - Automatic road detection in traffic videos
AU - Ghahremannezhad, Hadi
AU - Shi, Hang
AU - Liu, Chengjun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Automatic road detection is a challenging and representative computer vision problem due to a wide range of illumination variations and weather conditions in real traffic. This paper presents a novel real-time road detection method that is able to accurately and robustly extract the road region in real traffic videos under adverse illumination and weather conditions. Specifically, the innovative global foreground modeling (GFM) method is first applied to robustly model the ever-changing background in the traffic as well as to accurately detect the regions of the moving objects, namely the vehicles on the road. Note that the regions of the moving vehicles are reasonably assumed to be the road regions, which are then utilized to generate in total seven probability maps. In particular, four of these maps are derived using the color values in the RGB and HSV color spaces. Two additional probability maps are calculated from the two normalized histograms corresponding to the road and the non-road pixels in the RGB and grayscale color spaces, respectively. The last probability map is computed from the edges detected by the Canny edge detector and the regions located by the flood-fill algorithm. Finally, a novel automatic road detection method, which integrates these seven masks based on their probability values, defines a final probability mask for accurate and robust road detection in video.
AB - Automatic road detection is a challenging and representative computer vision problem due to a wide range of illumination variations and weather conditions in real traffic. This paper presents a novel real-time road detection method that is able to accurately and robustly extract the road region in real traffic videos under adverse illumination and weather conditions. Specifically, the innovative global foreground modeling (GFM) method is first applied to robustly model the ever-changing background in the traffic as well as to accurately detect the regions of the moving objects, namely the vehicles on the road. Note that the regions of the moving vehicles are reasonably assumed to be the road regions, which are then utilized to generate in total seven probability maps. In particular, four of these maps are derived using the color values in the RGB and HSV color spaces. Two additional probability maps are calculated from the two normalized histograms corresponding to the road and the non-road pixels in the RGB and grayscale color spaces, respectively. The last probability map is computed from the edges detected by the Canny edge detector and the regions located by the flood-fill algorithm. Finally, a novel automatic road detection method, which integrates these seven masks based on their probability values, defines a final probability mask for accurate and robust road detection in video.
KW - RoI determination
KW - Road detection
KW - Traffic video analytics
UR - http://www.scopus.com/inward/record.url?scp=85108028049&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85108028049&partnerID=8YFLogxK
U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00123
DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00123
M3 - Conference contribution
AN - SCOPUS:85108028049
T3 - Proceedings - 2020 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2020 IEEE International Conference on Big Data and Cloud Computing, 2020 IEEE International Symposium on Social Computing and Networking and 2020 IEEE International Conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020
SP - 777
EP - 784
BT - Proceedings - 2020 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2020 IEEE International Conference on Big Data and Cloud Computing, 2020 IEEE International Symposium on Social Computing and Networking and 2020 IEEE International Conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020
A2 - Hu, Jia
A2 - Min, Geyong
A2 - Georgalas, Nektarios
A2 - Zhao, Zhiwei
A2 - Hao, Fei
A2 - Miao, Wang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Symposium on Parallel and Distributed Processing with Applications, 10th IEEE International Conference on Big Data and Cloud Computing, 13th IEEE International Symposium on Social Computing and Networking and 10th IEEE International Conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020
Y2 - 17 December 2020 through 19 December 2020
ER -