@article{Lee_Lim_Kim_Sohng_2014, title={Improved Vehicle Detection Algorithm in Heavy Traffic for Intelligent Vehicle}, volume={20}, url={https://eejournal.ktu.lt/index.php/elt/article/view/3734}, DOI={10.5755/j01.eee.20.9.3734}, abstractNote={Despite significant progress in vehicle detection over the last few decades, vehicle detection performance in heavy traffic is still inadequate. In this paper, we propose a new algorithm for vehicle detection in heavy traffic to improve detection performance. It uses two proposed segmentation methods, namely, the disparity map-based bird’s-eye-view mapping segmentation method and the edge distance weighted conditional random field (CRF)-based segmentation method. Our experimental results show that the proposed algorithm outperforms conventional algorithms. The improvements in performance range from 10.8 % to 20.5 % increase in F-measure. <p>DOI: <a href="http://dx.doi.org/10.5755/j01.eee.20.9.3734">http://dx.doi.org/10.5755/j01.eee.20.9.3734</a></p>}, number={9}, journal={Elektronika ir Elektrotechnika}, author={Lee, Chung-Hee and Lim, Young-Chul and Kim, Dongyoung and Sohng, Kyu-Ik}, year={2014}, month={Nov.}, pages={54-58} }