Efficient algorithm for automatic road sign recognition and its hardware implementation

Souani, Chokri; Faiedh, Hassène; Besbes, Kamel

doi:10.1007/s11554-013-0348-z

Cited by 46 publications

(20 citation statements)

References 26 publications

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“…In another study, artificial neural network was used for recognition step in which the shape of a sign can be detected as circular, triangular, 'give way' panel shape, or stop panel shape. It also changes the color space into HSV for improving the recognition performance [32].…”

Section: Related Workmentioning

confidence: 99%

A novel fusion mixture of active experts algorithm for traffic signs recognition

Ahangi

Langroudi

Yazdanpanah

et al. 2019

Multimed Tools Appl

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Traffic sign recognition is an important problem in today's applications. In this paper, by combining ensemble and active learning methods, a novel fusion mixture of active experts algorithm is proposed for this problem. The active learning algorithm is a popular method for reducing the number of samples. The primary goal of active learning is diminishing complexity, increasing the convergence rate, speeding up training process, and decreasing the cost of samples labeling. The active learning, hence, chooses informative samples to train. In addition, ensemble methods are a combination of simple classifiers for improving accuracy. Each classifier tries to learn a region of dataset better than other regions that all opinions are considered on ensemble methods as an ultimate decision. The mixture of experts is one of the most modern hybrid methods in which the training process takes a relatively long time, and it is a problem for large datasets. Our proposed Mixture of Active Experts tries to solve this problem. It decreases the training time process and increases the speed of convergence for finding optimal weights by selecting only informative samples in active learning phase. It is also applicable for online situations, in which the model should be trained continuously. The results of different experiments on German Traffic Sign Recognition Benchmark dataset demonstrate that the proposed method shows 96.69% accuracy and achieved the 6th rank among all the state of the art algorithms using smaller number (only 60%) of training samples.

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Section: Related Workmentioning

confidence: 99%

A novel fusion mixture of active experts algorithm for traffic signs recognition

Ahangi

Langroudi

Yazdanpanah

et al. 2019

Multimed Tools Appl

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“…The authors in [2] used the ratios between the intensity of a given channel and the sum of all RGB channel intensities. The HSV (HueSaturation-Value) color space has been used in many works presented by Waite and Oruklu in [3] and authors in [4,5]. This color space is preferred as it is ineffective to illumination effects.…”

Section: Traffic Sign Stages 31 Preprocessing Techniquesmentioning

confidence: 99%

An Overview of Traffic Signs Recognition Methods

Dewan¹,

Vig²,

Shukla³

et al. 2017

IJCA

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Image processing has many areas of applications like in metrological prediction of weather conditions, medical science, artificial intelligence, robotics etc. Traffic signs recognition system is in fact the most happening area of research these days. As the world is moving towards the driverless vehicles, a more automated world is the utmost requirement for improving the road safety. Such systems can help the drivers on signs that they may not have noticed beforehand. These systems are so designed so that they consume less power and hence can be efficiently implemented on hardware. FPGA's are preferred over CPU and GPU due to its low cost and power, prototyping applications and next levels to ASIC's development. In this paper we have quoted some basic challenges in traffic signs recognition methods and summarized the various detection and recognition techniques for traffic signs. This paper divided the various methods into three categories: color-based, shape-based and learning-based. We have concluded that the Xilinx System Generator is the best tool while implementing on FPGA's. It is the fastest resource estimation tool in order to take full advantage of FPGA's resources. Finally, the hardware perspective of traffic signs implementation is briefly examined.

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“…Their weak points are poor optimization of available resources for performance and low energy consumption. Some examples of this new trend combine (i) multi-cores with FPGA for lane departure warning [ 20 ] and traffic sign recognition [ 21 , 22 ]; (ii) multi-cores with GPGPU [ 13 ]; and even, (iii) FPGA and GPGPU [ 23 ].…”

Section: State Of the Art In Embedded Systems For Multi-sensor Permentioning

confidence: 99%

A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters

Medina

Diez-Ochoa

Correal

et al. 2017

Sensors

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Grid-based perception techniques in the automotive sector based on fusing information from different sensors and their robust perceptions of the environment are proliferating in the industry. However, one of the main drawbacks of these techniques is the traditionally prohibitive, high computing performance that is required for embedded automotive systems. In this work, the capabilities of new computing architectures that embed these algorithms are assessed in a real car. The paper compares two ad hoc optimized designs of the Bayesian Occupancy Filter; one for General Purpose Graphics Processing Unit (GPGPU) and the other for Field-Programmable Gate Array (FPGA). The resulting implementations are compared in terms of development effort, accuracy and performance, using datasets from a realistic simulator and from a real automated vehicle.

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Efficient algorithm for automatic road sign recognition and its hardware implementation

Cited by 46 publications

References 26 publications

A novel fusion mixture of active experts algorithm for traffic signs recognition

A novel fusion mixture of active experts algorithm for traffic signs recognition

An Overview of Traffic Signs Recognition Methods

A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters

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