Speed Sign Recognition using Shape-based Features

Abukhait, Jafar; Zyout, Imad; Mansour, Ayman

doi:10.5120/14655-2964

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…In study [11], a speed recognition system has been proposed based on independent component analysis. In research [12], geometric features were deployed in the recognition of speed signs in the United States.…”

Section: Related Workmentioning

confidence: 99%

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

AbuKhait

2024

IJACSA

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This paper presents a fully-automated system for detecting road signs in the United States and assess their visibility during daytime from the perspective of the driver using images captured by an in-vehicle camera. The system deploys YOLOv8 to build a multi-label detection model and then, calculates various readability and detectability factors, including the simplicity of the surroundings, potential obstructions, and the angle at which the road sign is positioned, to determine the overall visibility of the sign. This proposed system can be integrated into Driver Assistance Systems (DAS) to manage the information delivered to drivers, as an excess of information could potentially distract them. Road signs are categorized based on their visibility levels, allowing Driver Assistance Systems to caution drivers about signs that may have lower visibility but are of significant importance. The system comprises four main stages: 1) identifying road signs using YOLOv8; 2) segmenting the surrounding areas; 3) measuring visibility parameters; and 4) determining visibility levels through fuzzy logic inference system. This paper introduces a visibility estimation system for road signs specifically tailored to the United States. Experimental results showcase the system's effectiveness. The visibility levels generated by the proposed system were subjectively compared to decisions made by human experts, revealing a substantial agreement between the two approaches.

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Section: Related Workmentioning

confidence: 99%

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

AbuKhait

2024

IJACSA

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“…Abukhait et al [22] use a shape-based detector to find US speed limit signs-note the model-based, rather than learning-based approach. The detector is part of a full recognition system, and the only reported performance figure is a detection rate of about 88%, but without mention of false positive rates.…”

Section: Related Studiesmentioning

confidence: 99%

Detection of U.S. Traffic Signs

Møgelmose

Liu

Trivedi

2015

IEEE Trans. Intell. Transport. Syst.

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This paper presents a comprehensive research study of the detection of U.S. traffic signs. Until now, the research in Traffic Sign Recognition systems has been centered on European traffic signs, but signs can look very different across different parts of the world, and a system that works well in Europe may indeed not work in the U.S. We go over the recent advances in traffic sign detection and discuss the differences in signs across the world. Then we present a comprehensive extension to the publicly available LISA-TS traffic sign data set, almost doubling its size, now with high-definition-quality footage. The extension is made with testing of tracking sign detection systems in mind, providing videos of traffic sign passes. We apply the Integral Channel Features and Aggregate Channel Features detection methods to U.S. traffic signs and show performance numbers outperforming all previous research on U.S. signs (while also performing similarly to the state of the art on European signs). Integral Channel Features have previously been used successfully for European signs, whereas Aggregate Channel Features have never been applied to the field of traffic signs. We take a look at the performance differences between the two methods and analyze how they perform on very distinctive signs, as well as white, rectangular signs, which tend to blend into their environment.

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“…Note that all of the above work has been done using European traffic signs. Only a few works explicitly take on American signs: In [20] a shape based detector and classifier is used for American speed limit signs. Detection results are not shown on their own, but the final recognition results are around 88% recognition rate, with no mention of the number of false detections.…”

Section: Related Workmentioning

confidence: 99%

Traffic sign detection for U.S. roads: Remaining challenges and a case for tracking

Møgelmose

Liu

Trivedi

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Traffic sign detection is crucial in intelligent vehicles, no matter if one's objective is to develop Advanced Driver Assistance Systems or autonomous cars. Recent advances in traffic sign detection, especially the great effort put into the competition German Traffic Sign Detection Benchmark, have given rise to very reliable detection systems when tested on European signs. The U.S., however, has a rather different approach to traffic sign design. This paper evaluates whether a current state-of-the-art traffic sign detector is useful for American signs. We find that for colorful, distinctively shaped signs, Integral Channel Features work well, but it fails on the large superclass of speed limit signs and similar designs.We also introduce an extension to the largest public dataset of American signs, the LISA Traffic Sign Dataset, and present an evaluation of tracking in the context of sign detection. We show that tracking essentially suppresses all false positives in our test set, and argue that in order to be useful for higher level analysis, any traffic sign detection system should contain tracking.

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Speed Sign Recognition using Shape-based Features

Cited by 9 publications

References 24 publications

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

US Road Sign Detection and Visibility Estimation using Artificial Intelligence Techniques

Detection of U.S. Traffic Signs

Traffic sign detection for U.S. roads: Remaining challenges and a case for tracking

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