28,669 research outputs found
Fast detection of multiple objects in traffic scenes with a common detection framework
Traffic scene perception (TSP) aims to real-time extract accurate on-road
environment information, which in- volves three phases: detection of objects of
interest, recognition of detected objects, and tracking of objects in motion.
Since recognition and tracking often rely on the results from detection, the
ability to detect objects of interest effectively plays a crucial role in TSP.
In this paper, we focus on three important classes of objects: traffic signs,
cars, and cyclists. We propose to detect all the three important objects in a
single learning based detection framework. The proposed framework consists of a
dense feature extractor and detectors of three important classes. Once the
dense features have been extracted, these features are shared with all
detectors. The advantage of using one common framework is that the detection
speed is much faster, since all dense features need only to be evaluated once
in the testing phase. In contrast, most previous works have designed specific
detectors using different features for each of these objects. To enhance the
feature robustness to noises and image deformations, we introduce spatially
pooled features as a part of aggregated channel features. In order to further
improve the generalization performance, we propose an object subcategorization
method as a means of capturing intra-class variation of objects. We
experimentally demonstrate the effectiveness and efficiency of the proposed
framework in three detection applications: traffic sign detection, car
detection, and cyclist detection. The proposed framework achieves the
competitive performance with state-of- the-art approaches on several benchmark
datasets.Comment: Appearing in IEEE Transactions on Intelligent Transportation System
Ego-Lane Analysis System (ELAS): Dataset and Algorithms
Decreasing costs of vision sensors and advances in embedded hardware boosted
lane related research detection, estimation, and tracking in the past two
decades. The interest in this topic has increased even more with the demand for
advanced driver assistance systems (ADAS) and self-driving cars. Although
extensively studied independently, there is still need for studies that propose
a combined solution for the multiple problems related to the ego-lane, such as
lane departure warning (LDW), lane change detection, lane marking type (LMT)
classification, road markings detection and classification, and detection of
adjacent lanes (i.e., immediate left and right lanes) presence. In this paper,
we propose a real-time Ego-Lane Analysis System (ELAS) capable of estimating
ego-lane position, classifying LMTs and road markings, performing LDW and
detecting lane change events. The proposed vision-based system works on a
temporal sequence of images. Lane marking features are extracted in perspective
and Inverse Perspective Mapping (IPM) images that are combined to increase
robustness. The final estimated lane is modeled as a spline using a combination
of methods (Hough lines with Kalman filter and spline with particle filter).
Based on the estimated lane, all other events are detected. To validate ELAS
and cover the lack of lane datasets in the literature, a new dataset with more
than 20 different scenes (in more than 15,000 frames) and considering a variety
of scenarios (urban road, highways, traffic, shadows, etc.) was created. The
dataset was manually annotated and made publicly available to enable evaluation
of several events that are of interest for the research community (i.e., lane
estimation, change, and centering; road markings; intersections; LMTs;
crosswalks and adjacent lanes). ELAS achieved high detection rates in all
real-world events and proved to be ready for real-time applications.Comment: 13 pages, 17 figures,
github.com/rodrigoberriel/ego-lane-analysis-system, and published by Image
and Vision Computing (IMAVIS
Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network
We propose a novel traffic sign detection system that simultaneously
estimates the location and precise boundary of traffic signs using
convolutional neural network (CNN). Estimating the precise boundary of traffic
signs is important in navigation systems for intelligent vehicles where traffic
signs can be used as 3D landmarks for road environment. Previous traffic sign
detection systems, including recent methods based on CNN, only provide bounding
boxes of traffic signs as output, and thus requires additional processes such
as contour estimation or image segmentation to obtain the precise sign
boundary. In this work, the boundary estimation of traffic signs is formulated
as a 2D pose and shape class prediction problem, and this is effectively solved
by a single CNN. With the predicted 2D pose and the shape class of a target
traffic sign in an input image, we estimate the actual boundary of the target
sign by projecting the boundary of a corresponding template sign image into the
input image plane. By formulating the boundary estimation problem as a
CNN-based pose and shape prediction task, our method is end-to-end trainable,
and more robust to occlusion and small targets than other boundary estimation
methods that rely on contour estimation or image segmentation. The proposed
method with architectural optimization provides an accurate traffic sign
boundary estimation which is also efficient in compute, showing a detection
frame rate higher than 7 frames per second on low-power mobile platforms.Comment: Accepted for publication in IEEE Transactions on Intelligent
Transportation System
Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
Autonomous driving is becoming a future practical lifestyle greatly driven by
deep learning. Specifically, an effective traffic sign detection by deep
learning plays a critical role for it. However, different countries have
different sets of traffic signs, making localized traffic sign recognition
model training a tedious and daunting task. To address the issues of taking
amount of time to compute complicate algorithm and low ratio of detecting
blurred and sub-pixel images of localized traffic signs, we propose Multi-Scale
Deconvolution Networks (MDN), which flexibly combines multi-scale convolutional
neural network with deconvolution sub-network, leading to efficient and
reliable localized traffic sign recognition model training. It is demonstrated
that the proposed MDN is effective compared with classical algorithms on the
benchmarks of the localized traffic sign, such as Chinese Traffic Sign Dataset
(CTSD), and the German Traffic Sign Benchmarks (GTSRB)
Real-time 3D Traffic Cone Detection for Autonomous Driving
Considerable progress has been made in semantic scene understanding of road
scenes with monocular cameras. It is, however, mainly related to certain
classes such as cars and pedestrians. This work investigates traffic cones, an
object class crucial for traffic control in the context of autonomous vehicles.
3D object detection using images from a monocular camera is intrinsically an
ill-posed problem. In this work, we leverage the unique structure of traffic
cones and propose a pipelined approach to the problem. Specifically, we first
detect cones in images by a tailored 2D object detector; then, the spatial
arrangement of keypoints on a traffic cone are detected by our deep structural
regression network, where the fact that the cross-ratio is projection invariant
is leveraged for network regularization; finally, the 3D position of cones is
recovered by the classical Perspective n-Point algorithm. Extensive experiments
show that our approach can accurately detect traffic cones and estimate their
position in the 3D world in real time. The proposed method is also deployed on
a real-time, critical system. It runs efficiently on the low-power Jetson TX2,
providing accurate 3D position estimates, allowing a race-car to map and drive
autonomously on an unseen track indicated by traffic cones. With the help of
robust and accurate perception, our race-car won both Formula Student
Competitions held in Italy and Germany in 2018, cruising at a top-speed of 54
kmph. Visualization of the complete pipeline, mapping and navigation can be
found on our project page.Comment: IEEE Intelligent Vehicles Symposium (IV'19). arXiv admin note: text
overlap with arXiv:1809.1054
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
Automatic detection and recognition of traffic signs plays a crucial role in
management of the traffic-sign inventory. It provides accurate and timely way
to manage traffic-sign inventory with a minimal human effort. In the computer
vision community the recognition and detection of traffic signs is a
well-researched problem. A vast majority of existing approaches perform well on
traffic signs needed for advanced drivers-assistance and autonomous systems.
However, this represents a relatively small number of all traffic signs (around
50 categories out of several hundred) and performance on the remaining set of
traffic signs, which are required to eliminate the manual labor in traffic-sign
inventory management, remains an open question. In this paper, we address the
issue of detecting and recognizing a large number of traffic-sign categories
suitable for automating traffic-sign inventory management. We adopt a
convolutional neural network (CNN) approach, the Mask R-CNN, to address the
full pipeline of detection and recognition with automatic end-to-end learning.
We propose several improvements that are evaluated on the detection of traffic
signs and result in an improved overall performance. This approach is applied
to detection of 200 traffic-sign categories represented in our novel dataset.
Results are reported on highly challenging traffic-sign categories that have
not yet been considered in previous works. We provide comprehensive analysis of
the deep learning method for the detection of traffic signs with large
intra-category appearance variation and show below 3% error rates with the
proposed approach, which is sufficient for deployment in practical applications
of traffic-sign inventory management.Comment: Accepted for publication in IEEE Transactions on Intelligent
Transportation System
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Recently, the advancement of deep learning in discriminative feature learning
from 3D LiDAR data has led to rapid development in the field of autonomous
driving. However, automated processing uneven, unstructured, noisy, and massive
3D point clouds is a challenging and tedious task. In this paper, we provide a
systematic review of existing compelling deep learning architectures applied in
LiDAR point clouds, detailing for specific tasks in autonomous driving such as
segmentation, detection, and classification. Although several published
research papers focus on specific topics in computer vision for autonomous
vehicles, to date, no general survey on deep learning applied in LiDAR point
clouds for autonomous vehicles exists. Thus, the goal of this paper is to
narrow the gap in this topic. More than 140 key contributions in the recent
five years are summarized in this survey, including the milestone 3D deep
architectures, the remarkable deep learning applications in 3D semantic
segmentation, object detection, and classification; specific datasets,
evaluation metrics, and the state of the art performance. Finally, we conclude
the remaining challenges and future researches.Comment: 21 pages, submitted to IEEE Transactions on Neural Networks and
Learning System
A Robust Lane Detection and Departure Warning System
In this work, we have developed a robust lane detection and departure warning
technique. Our system is based on single camera sensor. For lane detection a
modified Inverse Perspective Mapping using only a few extrinsic camera
parameters and illuminant Invariant techniques is used. Lane markings are
represented using a combination of 2nd and 4th order steerable filters, robust
to shadowing. Effect of shadowing and extra sun light are removed using Lab
color space, and illuminant invariant representation. Lanes are assumed to be
cubic curves and fitted using robust RANSAC. This method can reliably detect
lanes of the road and its boundary. This method has been experimented in Indian
road conditions under different challenging situations and the result obtained
were very good. For lane departure angle an optical flow based method were
used.Comment: The Intelligent Vehicles Symposium (IV2015). arXiv admin note: text
overlap with arXiv:1503.0664
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Deep learning-based techniques have achieved state-of-the-art performance on
a wide variety of recognition and classification tasks. However, these networks
are typically computationally expensive to train, requiring weeks of
computation on many GPUs; as a result, many users outsource the training
procedure to the cloud or rely on pre-trained models that are then fine-tuned
for a specific task. In this paper we show that outsourced training introduces
new security risks: an adversary can create a maliciously trained network (a
backdoored neural network, or a \emph{BadNet}) that has state-of-the-art
performance on the user's training and validation samples, but behaves badly on
specific attacker-chosen inputs. We first explore the properties of BadNets in
a toy example, by creating a backdoored handwritten digit classifier. Next, we
demonstrate backdoors in a more realistic scenario by creating a U.S. street
sign classifier that identifies stop signs as speed limits when a special
sticker is added to the stop sign; we then show in addition that the backdoor
in our US street sign detector can persist even if the network is later
retrained for another task and cause a drop in accuracy of {25}\% on average
when the backdoor trigger is present. These results demonstrate that backdoors
in neural networks are both powerful and---because the behavior of neural
networks is difficult to explicate---stealthy. This work provides motivation
for further research into techniques for verifying and inspecting neural
networks, just as we have developed tools for verifying and debugging software
Optimized Method for Iranian Road Signs Detection and recognition system
Road sign recognition is one of the core technologies in Intelligent
Transport Systems. In the current study, a robust and real-time method is
presented to identify and detect the roads speed signs in road image in
different situations. In our proposed method, first, the connected components
are created in the main image using the edge detection and mathematical
morphology and the location of the road signs extracted by the geometric and
color data; then the letters are segmented and recognized by Multiclass Support
Vector Machine (SVMs) classifiers. Regarding that the geometric and color
features ate properly used in detection the location of the road signs, so it
is not sensitive to the distance and noise and has higher speed and efficiency.
In the result part, the proposed approach is applied on Iranian road speed sign
database and the detection and recognition accuracy rate achieved 98.66% and
100% respectively
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