9,212 research outputs found
An Empirical Evaluation of Deep Learning on Highway Driving
Numerous groups have applied a variety of deep learning techniques to
computer vision problems in highway perception scenarios. In this paper, we
presented a number of empirical evaluations of recent deep learning advances.
Computer vision, combined with deep learning, has the potential to bring about
a relatively inexpensive, robust solution to autonomous driving. To prepare
deep learning for industry uptake and practical applications, neural networks
will require large data sets that represent all possible driving environments
and scenarios. We collect a large data set of highway data and apply deep
learning and computer vision algorithms to problems such as car and lane
detection. We show how existing convolutional neural networks (CNNs) can be
used to perform lane and vehicle detection while running at frame rates
required for a real-time system. Our results lend credence to the hypothesis
that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio
Pedestrian lane detection in unstructured scenes for assistive navigation
Automatic detection of the pedestrian lane in a scene is an important task in assistive and autonomous navigation. This paper presents a vision-based algorithm for pedestrian lane detection in unstructured scenes, where lanes vary significantly in color, texture, and shape and are not indicated by any painted markers. In the proposed method, a lane appearance model is constructed adaptively from a sample image region, which is identified automatically from the image vanishing point. This paper also introduces a fast and robust vanishing point estimation method based on the color tensor and dominant orientations of color edge pixels. The proposed pedestrian lane detection method is evaluated on a new benchmark dataset that contains images from various indoor and outdoor scenes with different types of unmarked lanes. Experimental results are presented which demonstrate its efficiency and robustness in comparison with several existing methods
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
We present a novel approach for vanishing point detection from uncalibrated
monocular images. In contrast to state-of-the-art, we make no a priori
assumptions about the observed scene. Our method is based on a convolutional
neural network (CNN) which does not use natural images, but a Gaussian sphere
representation arising from an inverse gnomonic projection of lines detected in
an image. This allows us to rely on synthetic data for training, eliminating
the need for labelled images. Our method achieves competitive performance on
three horizon estimation benchmark datasets. We further highlight some
additional use cases for which our vanishing point detection algorithm can be
used.Comment: Accepted for publication at German Conference on Pattern Recognition
(GCPR) 2017. This research was supported by German Research Foundation DFG
within Priority Research Programme 1894 "Volunteered Geographic Information:
Interpretation, Visualisation and Social Computing
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