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

Jeremy Pinto, Nolan Lunscher, Georges Younes, David Abou Chacra, Henry Leopold, John Zelek


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.

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