@article{Pinto_Lunscher_Younes_Chacra_Leopold_Zelek_2016, title={Spatial Detection of Vehicles in Images using Convolutional Neural Networks and Stereo Matching}, volume={2}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/101}, DOI={10.15353/vsnl.v2i1.101}, abstractNote={<p>Convolutional Neural Networks combined with a state of the art<br />stereo-matching method are used to find and estimate the 3D position<br />of vehicles in pairs of stereo images. Pixel positions of vehicles<br />are first estimated separately in pairs of stereo images using<br />a Convolutional Neural Network for regression. These coordinates<br />are then combined with a state-of-art stereo-matching method to<br />determine the depth, and thus the 3D location, of the vehicles. We<br />show in this paper that cars can be detected with a combined accuracy<br />of approximately 90% with a tolerated radius error of 5%,<br />and a Mean Absolute Error of 5.25m on depth estimation for cars<br />up to 50m away.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Pinto, Jeremy and Lunscher, Nolan and Younes, Georges and Chacra, David Abou and Leopold, Henry and Zelek, John}, year={2016}, month={Oct.} }