3,113 research outputs found
Fast traffic sign recognition using color segmentation and deep convolutional networks
The use of Computer Vision techniques for the automatic
recognition of road signs is fundamental for the development of intelli-
gent vehicles and advanced driver assistance systems. In this paper, we
describe a procedure based on color segmentation, Histogram of Ori-
ented Gradients (HOG), and Convolutional Neural Networks (CNN) for
detecting and classifying road signs. Detection is speeded up by a pre-
processing step to reduce the search space, while classication is carried
out by using a Deep Learning technique. A quantitative evaluation of the
proposed approach has been conducted on the well-known German Traf-
c Sign data set and on the novel Data set of Italian Trac Signs (DITS),
which is publicly available and contains challenging sequences captured
in adverse weather conditions and in an urban scenario at night-time.
Experimental results demonstrate the eectiveness of the proposed ap-
proach in terms of both classication accuracy and computational speed
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision
and robotics communities, where the task is to accurately and efficiently
recognize the location of a given query image. Despite years of wisdom
accumulated in this field, place recognition still remains an open problem due
to the various ways in which the appearance of real-world places may differ.
This paper presents an overview of the place recognition literature. Since
condition invariant and viewpoint invariant features are essential factors to
long-term robust visual place recognition system, We start with traditional
image description methodology developed in the past, which exploit techniques
from image retrieval field. Recently, the rapid advances of related fields such
as object detection and image classification have inspired a new technique to
improve visual place recognition system, i.e., convolutional neural networks
(CNNs). Thus we then introduce recent progress of visual place recognition
system based on CNNs to automatically learn better image representations for
places. Eventually, we close with discussions and future work of place
recognition.Comment: Applied Sciences (2018
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication
- …