TY - GEN
T1 - A New Adaptive Bidirectional Region-of-Interest Detection Method for Intelligent Traffic Video Analysis
AU - Ghahremannezhad, Hadi
AU - Shi, Hang
AU - Liu, Chengjun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Real-time intelligent video-based traffic surveillance applications play an important role in intelligent transportation systems. To reduce false alarms as well as to increase computational efficiency, robust road segmentation for automated Region of Interest (RoI) detection becomes a popular focus in the research community. A novel Adaptive Bidirectional Detection (ABD) of region-of-interest method is presented in this paper to automatically segment the roads with bidirectional traffic flows into two regions of interest. Specifically, a foreground segmentation method is first applied along with the flood-fill algorithm to estimate the road regions. Then the Lucas-Kanade's optical flow algorithm is utilized to track and divide the estimated road into regions of interest in real-time. Experimental results using a dataset of real traffic videos illustrate the feasibility of the proposed method for automatically determining the RoIs in real-time.
AB - Real-time intelligent video-based traffic surveillance applications play an important role in intelligent transportation systems. To reduce false alarms as well as to increase computational efficiency, robust road segmentation for automated Region of Interest (RoI) detection becomes a popular focus in the research community. A novel Adaptive Bidirectional Detection (ABD) of region-of-interest method is presented in this paper to automatically segment the roads with bidirectional traffic flows into two regions of interest. Specifically, a foreground segmentation method is first applied along with the flood-fill algorithm to estimate the road regions. Then the Lucas-Kanade's optical flow algorithm is utilized to track and divide the estimated road into regions of interest in real-time. Experimental results using a dataset of real traffic videos illustrate the feasibility of the proposed method for automatically determining the RoIs in real-time.
KW - RoI determination
KW - real time applications
KW - road detection
KW - traffic video analysis
UR - http://www.scopus.com/inward/record.url?scp=85102400266&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102400266&partnerID=8YFLogxK
U2 - 10.1109/AIKE48582.2020.00012
DO - 10.1109/AIKE48582.2020.00012
M3 - Conference contribution
AN - SCOPUS:85102400266
T3 - Proceedings - 2020 IEEE 3rd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020
SP - 17
EP - 24
BT - Proceedings - 2020 IEEE 3rd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020
Y2 - 9 December 2020 through 11 December 2020
ER -