MONet - Multiple Output Network for Driver Assistance Systems Based on a Monocular Camera

Deep learning based image processing has become popular and approaches using convolutional neural networks (CNNs) have been widely used in recent years. In this paper we propose a multiple output convolutional neural network for road traffic scene understanding using a monocular camera. The color images are fed into the artificial neural network that produces multiple outputs. Our model performs three tasks: semantic segmentation, object detection and vanishing point computation. The semantic segmentation produces relevant data regarding the traffic scene, the obstacle detection module provides individual obstacles, whereas the vanishing point module will provide information that can be used to perform extrinsic camera calibration. We propose a novel obstacle detection approach and extend already published work by having a vanishing point detection module. The multiple outputs are predicted in a single-step and the information can be used as an initialization step for a 3D tracking system. Our network can extract individual dynamic objects and their correlation to the 3D space can be computed using the extrinsic parameters generated from the vanishing point module.