An online traffic sign recognition system for intelligent driver assistance

Manisha, U.K.D.N.; Liyanage, S.R.

doi:10.1109/icter.2017.8257815

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Soni et al [ 24 ] processed the Chinese traffic sign dataset through SVM, trained on the HOG or LBP after Principal Component Analysis (PCA), reaching an accuracy of 84.44%. A similar setup was used by Manisha and Liyanage [ 21 ], who achieved 98.6% accuracy on vehicles moving at 40–45 km/h. Moreover, Matoš et al [ 22 ] used an SVM trained on HOG features and achieved recognition of 93.75% accuracy on the GTSRB dataset.…”

Section: Background On Traffic Sign Recognitionmentioning

confidence: 99%

“…Alternatively, deep learning algorithms, such as AlexNet, googLeNet [ 20 ] can directly process images coming from sensors and classify them according to internal representation learning processes, which are orchestrated through multiple convolutional and fully connected layers. Throughout the years, many studies tackled TSR [ 21 , 22 , 23 ] using different feature descriptors and ML-based classifiers. Different combinations of such classifiers and features have been proven to generate heterogeneous classification scores [ 15 , 19 , 24 , 25 , 26 , 27 ] motivating the need for comparisons to discover the optimal classifier for a given TSR problem [ 3 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Towards Enhancing Traffic Sign Recognition through Sliding Windows

Atif¹,

Zoppi²,

Gharib

et al. 2022

Sensors

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Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR.

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Section: Background On Traffic Sign Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Atif¹,

Zoppi²,

Gharib

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In the last decade, researchers and practitioners used nondeep ML classifiers for single-frame TSR [30], [31], [32]. Supervised classifiers such as Support Vector Machines (SVMs, [33]), Decision Trees (and ensembles, [34], [35]) or Nearest Neighbors (KNN, [36]) cannot directly process frames.…”

Section: A Traffic Sign Recognitionmentioning

confidence: 99%

Robust Traffic Sign Recognition Against Camera Failures

Atif

Ceccarelli

Zoppi

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Failures of the vehicle camera may compromise the correct acquisition of frames, that are subsequently used by autonomous driving tasks. A clear understanding of the behavior of the autonomous driving tasks under such failure conditions, together with strategies to avoid safety is jeopardized, are indeed necessary. This study analyses and improve the performance of Traffic Sign Recognition (TSR) systems for road vehicles under the possible occurrence of camera failures. Our experimental assessment relies on three public datasets, which are commonly used for benchmarking TSR systems. We artificially inject 13 different types of camera failures into the three datasets. Then, we exploit three deep neural networks (DNNs) to classify either a single frame of a traffic sign or a sequence (i.e., a sliding window) of frames. We show that sliding windows significantly improves the robustness of the classifier against altered frames. We confirm our observations through explainable AI, which allows understanding why different classifiers have different performance in case of camera failures.

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“…For instance, the GTSDB is used in [6][7][8][9]. On the other hand, researches in [10][11][12] used the MTSD dataset for the implementation of the TSR.…”

mentioning

confidence: 99%

A Real-Time Malaysian Traffic Sign Recognition Using YOLO Algorithm

Mangshor

Paudzi

Ibrahim

et al. 2021

Lecture Notes in Electrical Engineering

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In recent years, research towards Autonomous Vehicle (AV) has grown up tremendously. The introduction of Advanced Driving Assistance System (ADAS) in AV has led researchers to explore on the key functionalities that an AV should have, proposes to create a convenience and safe environment where there is no input acquire from a human throughout the journey. One of the key functionalities is the Traffic Sign Recognition (TSR). Hence, this study aims to develop a real-time traffic sign recognition system that can recognize Malaysian traffic sign in real-time and to test the accuracy of the system. The Deep Learning technology using You Only Look Once (YOLO) algorithm is used to train the model for recognition. Five types of warning traffic sign including crossroad, crossroad right, crossroad left, school children crossing and bump from Malaysian traffic sign is used. The test results reached for real-time object detection is 96.00% accuracy on both traffic sign detection and recognition. The finding from this study is believed to be helpful as it may contribute to the ADAS development in the automotive industry for the self-driving vehicle. KeywordsTraffic sign recognition • Autonomous vehicle • YOLO 1 Introduction Nowadays, the automotive industries are vigorously exploring Autonomous Vehicle (AV) which includes the self-driving programs [1]. This technology has been growing instantaneously throughout the world [2]. The AV is a vehicle that the computers take the lead the art of self-driving program [3] using the integration of Advanced Driving

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An online traffic sign recognition system for intelligent driver assistance

Cited by 5 publications

References 15 publications

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Robust Traffic Sign Recognition Against Camera Failures

A Real-Time Malaysian Traffic Sign Recognition Using YOLO Algorithm

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