Cost-Efficient Traffic Sign Detection Relying on Smart Mobile Devices

Aminian, Mohammad S.; Allamehzadeh, Arman; Mostaed, Mehran; Olaverri-Monreal, Cristina

doi:10.1007/978-3-319-74727-9_50

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…The HSI color models are the most popular as it depends on the human color observation. In addition, it is generally considered invariant to changes in lighting and brightness [6][7][8]. HSI is used by many researchers such as [9][10][11][12]with the reason that the HSI space models is superior to RGB for human vision and permits some variety in the intensity of light.…”

Section: A Color Segmentationmentioning

confidence: 99%

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Audio Augmentation for Traffic Signs: A Case Study of Pakistani Traffic Signs

Wahab¹,

Khan²,

Rabbi³

et al. 2018

ijacsa

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Augmented Reality (AR) extend the appearance of real-world by adding digital information to the scene using computer graphics and image processing techniques. Various approaches have been used to detect, identify and track objects in real environment depending upon the application, shape of the tracking object and environment type. The marker-based tracking technique is the most commonly used method in augmented reality applications in which fiducial markers are put in the real-world for tracking. In this work we proposed a model to detect and identify the traffic signs through marker based technique to improve the usability of marker-based detection in augmented reality applications. We developed an AR application that can detect and recognize the markers designed for Pakistani traffic signs and augment them with voice alert to the driver so that he does prepared for the upcoming hurdle on the road. As identified by literature no work has been performed on augmentation of voice for traffic signs. From experiments the model outperforms than baseline techniques.

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Section: A Color Segmentationmentioning

confidence: 99%

“…A new approach of shape-based identification is the Haar cascade classifier which needs training a classifier for each traffic sign, prompting a tedious identification process that was tea large amount of the processor time as applied in [6].…”

Section: B Shape Based Detectionmentioning

confidence: 99%

Audio Augmentation for Traffic Signs: A Case Study of Pakistani Traffic Signs

Wahab¹,

Khan²,

Rabbi³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…Traffic sign recognition is a research hotspot in the application of visual navigation and computer vision in intelligent driving [1,2]. Under multiple constraints, the recognition of traffic signs needs to realize various goals with a high accuracy through complex implementation methods.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Traffic Signs Based on Visual Inspection

Chen

Zhang

et al. 2021

IEEE Access

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The automatic recognition of traffic signs is essential to autonomous driving, assisted driving, and driving safety. Currently, convolutional neural network (CNN) is the most popular deep learning algorithm in traffic sign recognition. However, the CNN cannot capture the poses, perspectives, and directions of the image, nor accurately recognize traffic signs from different perspectives. To solve the problem, the authors presented an automatic recognition algorithm for traffic signs based on visual inspection. For the accuracy of visual inspection, a region of interest (ROI) extraction method was designed through content analysis and key information recognition. Besides, a Histogram of Oriented Gradients (HOG) method was developed for image detection to prevent projection distortion. Furthermore, a traffic sign recognition learning architecture was created based on CapsNet, which relies on neurons to represent target parameters like dynamic routing, path pose and direction, and effectively capture the traffic sign information from different angles or directions. Finally, our model was compared with several baseline methods through experiments on LISA (Laboratory for Intelligent and Safe Automobiles) traffic sign dataset. The model performance was measured by mean average precision (MAP), time, memory, floating point operations per second (FLOPS), and parameter number. The results show that our model consumed shorter time yet better recognition performance than baseline methods, including CNN, support vector machine (SVM), and regionbased fully convolutional network (R-FCN) ResNet 101.

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Cost-Efficient Traffic Sign Detection Relying on Smart Mobile Devices

Cited by 2 publications

References 4 publications

Audio Augmentation for Traffic Signs: A Case Study of Pakistani Traffic Signs

Audio Augmentation for Traffic Signs: A Case Study of Pakistani Traffic Signs

Automatic Recognition of Traffic Signs Based on Visual Inspection

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