Research on recognition method of vehicle lane-change behavior based on video image
- 10.2991/asei-15.2015.2How to use a DOI?
- video image processing technology, HOUGH , HOG&SVM, lane change.
The research statistics show that about 60%-70% traffic accidents are caused by vehicle collisions. And lane change as the most common behavior when driving a vehicle is also the main reason for vehicle collisions. This paper based on the theories of video image processing technology, adopts the improved method of HOUGH to extract and fit the lane line. Through multi-threshold setting we determine the underneath shadow and further position the region of interest of the vehicle (ROI). In the case of offline, take features of large number of positive and negative HOG samples, and use the SVM to do the classified training. After extracting the HOG features, combine them with SVM classifier to achieve recognition of the vehicle. Then position the center of the vehicle, and use the binocular ranging method to get the distance between the vehicle and the lane lines. Finally, calculate the dispersion of the distance and set appropriate thresholds and determine whether the vehicle have the lane change behavior.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Rongbao Chen AU - Xiaoer Ye AU - Fengyan Zhang AU - Dan Zhao PY - 2015/05 DA - 2015/05 TI - Research on recognition method of vehicle lane-change behavior based on video image BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 5 EP - 10 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.2 DO - 10.2991/asei-15.2015.2 ID - Chen2015/05 ER -