Spatial Detection of Vehicles in Images using Convolutional Neural Networks and Stereo Matching

Convolutional Neural Networks combined with a state of the art stereo-matching method are used to find and estimate the 3D position of vehicles in pairs of stereo images. Pixel positions of vehicles are first estimated separately in pairs of stereo images using a Convolutional Neural Network for regression. These coordinates are then combined with a state-of-art stereo-matching method to determine the depth, and thus the 3D location, of the vehicles. We show in this paper that cars can be detected with a combined accuracy of approximately 90% with a tolerated radius error of 5%, and a Mean Absolute Error of 5.25m on depth estimation for cars up to 50m away.