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Road Detection through Supervised Classification
[article]
2016
arXiv
pre-print
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Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learning tasks; second, we introduce a road detection framework on this dataset through supervised classification ...
In this paper we focus on road detection through gray-scale images as the sole sensory input. ...
We examine a supervised classification approach to road area detection on this dataset and present our initial findings in Section IV. ...
arXiv:1605.03150v1
fatcat:r6shqqktrvakrfhwaprlo2cpa4
Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles
2020
KSII Transactions on Internet and Information Systems
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We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. ...
unsupervised identifications using image processing and supervised vision classifiers. ...
Object detections on Road
Conclusion In this study, we developed an intelligent hybrid fusion classification model to continuously and accurately detect road lanes using vision data, for the generation ...
doi:10.3837/tiis.2020.10.002
fatcat:oycsczl7dzaydn7wezxoe7ixne
A Self-Supervised Learning Framework for Road Centerline Extraction From High-Resolution Remote Sensing Images
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Compared with the supervised, the unsupervised, and the one-class classification road extraction algorithms, this proposed algorithm achieves high accuracy and efficiency. ...
The shape feature and the posterior probability are combined to form the final road network in the object-oriented way. Finally, the road centerline is obtained through the tensor voting algorithm. ...
Comparing With Supervised Road Extraction Algorithm In order to compare with the supervised road extraction algorithm, the algorithms in literature [17] , [16] are chosen. ...
doi:10.1109/jstars.2020.3014242
fatcat:u6ejjszqgnawndrv2d2zdmddgq
AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING
2012
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. ...
Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. ...
UPDATING TOPOGRAPHIC DATABASES THROUGH IMAGE CLASSIFICATION
PIXEL-BASED CLASSIFICATION
Supervised classification Using the maximum likelihood classification method, Walter & Fritsch (1998) found ...
doi:10.5194/isprsarchives-xxxix-b7-311-2012
fatcat:3psufpoi4ffcvn6r2uxf6lhtpy
Semantic Classification of Road Markings from Geometric Primitives
2018
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
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Firstly, we employ a weakly supervised neural network to detect pixels belonging to road markings under different conditions. ...
Unlike other methods in the literature that perform road marking classification independently, our proposed approach performs a joint classification leveraging the highly structured configurations that ...
In this paper, we propose an integrated framework for the detection and semantic classification of road markings. ...
doi:10.1109/itsc.2018.8569382
dblp:conf/itsc/AmayoB018
fatcat:mnpxsb5z4jegbhptm3tagbca4m
Reliable Event Detection via Multiple Edge Computing on Streaming Traffic Social Data
2021
IEEE Access
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The results indicate that our model can better implement streaming social traffic event detection, and is superior to most text classification methods. ...
Then, we utilize graph neural networks to perform semi-supervised learning on HIN to obtain the optimal meta-path weights. ...
We will introduce how to get ω through semi-supervised learning on HIN in Section V. ...
doi:10.1109/access.2021.3060624
fatcat:5finlsbexjam7jw6cy4rkbb6zy
A participatory sensing framework to classify road surface quality
2019
Journal of Internet Services and Applications
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A classification system aggregates the data, filters them, and extracts a set of features as input for supervised learning algorithms. ...
This article proposes Streetcheck, a framework to classify road surface quality through participatory sensing. ...
Furthermore, authors attempt to collect data at a constant speed and through roads with clear different conditions. ...
doi:10.1186/s13174-019-0111-1
fatcat:g5pxw5hbnjfjfhrankrh7sgkje
Inundated Areas Extraction Based on Raindrop Photometric Model (RPM) in Surveillance Video
2018
Water
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Constrained information, especially road ranges, was obtained from video background image which has eliminated interference factors. ...
Figure 14 . 14 The results of supervised classification.
Figure 14 . 14 The results of supervised classification.
Table 1 . 1 Video Data Properties. ...
outermost road lines or image boundary are detected as the road ranges. ...
doi:10.3390/w10101332
fatcat:jpnrmkmktjdcrdqicodieugjmi
Modern drowsiness detection techniques: a review
2022
International Journal of Power Electronics and Drive Systems (IJPEDS)
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The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types ...
of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion ...
In this paper we give a brief information about dataset, workflow of supervised machine learning, features, classification techniques that used for detecting driver drowsiness. ...
doi:10.11591/ijece.v12i3.pp2986-2995
fatcat:nhgr66t7gjd3nd7pmg5qz5rbsy
Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning
2019
Sensors
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In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. ...
First, the training DB is collected through the camera installed in the vehicle while driving on the road. ...
Creating the Training DB We collected training datasets through cameras installed on a vehicle while driving on an actual road in South Korea to train the neural network model to automatically detect road ...
doi:10.3390/s19245501
pmid:31842513
pmcid:PMC6961057
fatcat:uelv2ovw65bdpjaf5fxevlecmq
Combination of Textural Features for the Improvement of Terrain Classification and Change Detection
2015
International Journal of Software Engineering and Its Applications
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To verify the efficiency of the proposed classification method, change detection using temporal images is also tested via experiment. ...
The resulting change map shows that a newly developed area can be successfully detected. ...
Our method consists of two main stages concerned with supervised classification and change detection techniques. ...
doi:10.14257/ijseia.2015.9.5.14
fatcat:clavvgddv5fntjz6kmpdxhhiyi
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
[article]
2019
arXiv
pre-print
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In this paper, we present a survey on relevant visual surveillance related researches for anomaly detection in public places, focusing primarily on roads. ...
Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. ...
Gaussian Regression (GR) [147] A generic supervised learning method designed to solve regression and probabilistic classification problems: Used in [34, 153] for anomaly detection from videos. ...
arXiv:1901.08292v1
fatcat:qehtkb2imfbmpfahkgsjrx7544
Classification of the Acoustics of Loose Gravel
2021
Sensors
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Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. ...
In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network ...
Most of the studies for road type detection or road defects detection of paved roads have focused on the paved road [5] [6] [7] . ...
doi:10.3390/s21144944
fatcat:sodroocwarcx3pzudna2uxnm2y
Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil
2020
Journal of Geographic Information System
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In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness ...
Updating road networks through the use of hyperspectral remote sensing images can be a good alternative. ...
Reconstruction of Segments Missing from the Road Network Through the classification process based on defined rules, it was possible to delineate the most relevant sections of the road network present in ...
doi:10.4236/jgis.2020.121002
fatcat:3xvib6yp4zfxhklmnkpwvgunam
An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking
2021
Complexity
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While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. ...
Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with ...
Acknowledgments e authors would like to thank the United States (US) Government and Chicago Transit Authority (CTA) for providing the road incidental data. ...
doi:10.1155/2021/5543698
fatcat:hvjnzekvobetrow4vkq36iejky
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