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Road Detection through Supervised Classification [article]

Yasamin Alkhorshid, Kamelia Aryafar, Sven Bauer, Gerd Wanielik
2016 arXiv   pre-print
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  
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

Qing Guo, Zhipan Wang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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


P. Duncan, J. Smit
2012 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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

Paul Amayo, Tom Bruls, Paul Newman
2018 2018 21st International Conference on Intelligent Transportation Systems (ITSC)  
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

Yipeng Ji, Jingyi Wang, Yan Niu, Hongyuan Ma
2021 IEEE Access  
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

Davidson E. Nunes, Vinicius F. S. Mota
2019 Journal of Internet Services and Applications  
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

Yunzhe Lv, Wei Gao, Chen Yang, Ning Wang
2018 Water  
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

Sarah Saadoon Jasim, Alia Karim Abdul Hassan
2022 International Journal of Power Electronics and Drive Systems (IJPEDS)  
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

Chanjun Chun, Seung-Ki Ryu
2019 Sensors  
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

Hoang Lam Le, Dong-Min Woo
2015 International Journal of Software Engineering and Its Applications  
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]

Santhosh Kelathodi Kumaran, Debi Prosad Dogra, Partha Pratim Roy
2019 arXiv   pre-print
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

Nausheen Saeed, Roger G. Nyberg, Moudud Alam, Mark Dougherty, Diala Jooma, Pascal Rebreyend
2021 Sensors  
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

Márcia Macedo, Maria Maia, Emilia Kohlman Rabbani, Oswaldo Lima Neto
2020 Journal of Geographic Information System  
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

Zafar Iqbal, Majid I. Khan, Shahid Hussain, Asad Habib, Átila Bueno
2021 Complexity  
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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