This paper proposes and validates a real-time onroad vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows.
Reference:
Kanjee, R. Bachoo, A.K. and Carroll, J. 2015. A three-step vehicle detection framework for range estimation using a single camera. In: IEEE Symposium Series on Computational Intelligence 2015, Cape Town, 8-10 December 2015
Kanjee, R., Bachoo, A., & Carroll, J. (2015). A three-step vehicle detection framework for range estimation using a single camera. IEEE Xplore. http://hdl.handle.net/10204/8744
Kanjee, R, AK Bachoo, and J Carroll. "A three-step vehicle detection framework for range estimation using a single camera." (2015): http://hdl.handle.net/10204/8744
Kanjee R, Bachoo A, Carroll J, A three-step vehicle detection framework for range estimation using a single camera; IEEE Xplore; 2015. http://hdl.handle.net/10204/8744 .
IEEE Symposium Series on Computational Intelligence 2015, Cape Town, 8-10 December 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website
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