Robust road marking detection using convex grouping method in around-view monitoring system

Hyeon, Daejin; Lee, Soomok; Jung, Seung-Won; Kim, Seong-Woo; Seo, Seung‐Woo

doi:10.1109/ivs.2016.7535511

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…Hybrid model using SVM is superior to hybrid model using AdaBoost. But altogether the detection performance of the proposed hybrid model is better than other methods, in which baseline method [3] is the worst, followed by KB2010 [18] and K-NN (K-Nearest Neighbor) [5]. Although Random Forest has higher precision ratio, the whole detection performance is poor due to the lower recall rate.…”

Section: Experimental Results Analysismentioning

confidence: 89%

“…Road marking description step extracts candidate targets from the background generally using MSER [3,17]. Arrow marking recognition step uses classifier [3][4][5][6] such as SVM, Random Forest, and Neural Network to achieve arrows classification. Algorithm's flowchart is shown in Figure 1. …”

Section: The Proposed Hybrid Modelmentioning

confidence: 99%

“…In order to achieve all-day detection and identification of arrow markings in complex environment, much progress has been made in recent years [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The detection and recognition algorithms are generally classified into discriminative models [3][4][5][6] and biologically visual perceptual models [7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Discriminative models achieve the detection and recognition of arrow markings based on classifier (SVM (Support Vector Machine) [3,4], Random Forest [5], ANN (Artificial Neural Network) [6], etc.). Arrow markings in [3] are recognized by SVM using the HOG (Histogram of Oriented Gradient) features under the influence of shadows and illumination invariant.…”

Section: Introductionmentioning

confidence: 99%

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Biologically Visual Perceptual Model and Discriminative Model for Road Markings Detection and Recognition

Jia

Wei

et al. 2018

Mathematical Problems in Engineering

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The detection and recognition of arrow markings is a basic task of autonomous driving. To achieve all-day detection and recognition of arrow markings in complex environment, we propose a hybrid model by exploiting the advantages of biologically visual perceptual model and discriminative model. Firstly, the arrow markings are extracted from the complex background in the region of interest (ROI) by the biologically visual perceptual model using the frequency-tuned (FT) algorithm. Then candidates for road markings are detected as maximally stable extremal regions (MSER). In recognition stage, biologically visual perceptual model calculates the sparse solution of arrow markings using sparse learning theory. Finally, discriminative model uses the Adaptive Boosting (AdaBoost) classifier trained by sparse solution to classify arrow markings. Experimental results show that the hybrid model achieves detection and recognition of arrow markings in complex road conditions with the precision, recall, and F-measure being 0.966, 0.88, and 0.92, respectively. The hybrid model is robust and has some advantages compared with other state-of-the-art methods. The hybrid model proposed in this paper has important theoretical significance and practical value for all-day detection and recognition in complex environment.

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Section: Experimental Results Analysismentioning

confidence: 89%

Section: The Proposed Hybrid Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Biologically Visual Perceptual Model and Discriminative Model for Road Markings Detection and Recognition

Jia

Wei

et al. 2018

Mathematical Problems in Engineering

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“…In another study, Ahmad et al [ 58 ] consider weather and lighting conditions in the context of road marking. They consider various messages as distinct categories, while most systems [ 59 , 60 ] use OCR-based algorithms to detect letters first and then write. Unlike stormy, rainy days, dark conditions are created and lighting on the bright sunny day.…”

Section: Related Workmentioning

confidence: 99%

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Chy

Masum

Sayeed

et al. 2021

Sensors

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The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.

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Machine Learning Based Framework for Recognizing Traffic Signs on Road Surfaces

Gupta

Choudhary

2020

Advances in Intelligent Systems and Computing

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Robust road marking detection using convex grouping method in around-view monitoring system

Cited by 13 publications

References 18 publications

Biologically Visual Perceptual Model and Discriminative Model for Road Markings Detection and Recognition

Biologically Visual Perceptual Model and Discriminative Model for Road Markings Detection and Recognition

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Machine Learning Based Framework for Recognizing Traffic Signs on Road Surfaces

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