Abstract
We propose an algorithm to detect vehicles in front of an ego-vehicle using the soft cascade AdaBoost algorithm and a KLT feature tracker-based tracking method for road traffic scene at night. The proposed algorithm enhances the previous algorithm [4] in terms of processing time and vehicle detection performance. The algorithm also improves the tracking performance of the detected vehicles between consecutive image frames even with abrupt changes in the pitch or yaw motion of the ego-vehicle. We also produce a learning data set by exploiting an active AdaBoost learning scheme, which further improves classifier performance. The experimental results for real road images illustrate the effectiveness of the proposed algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Institute of Control, Robotics and Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
