A smart driver alert system for vehicle traffic using image detection and recognition technique

Harini, S.; Abhiram, V; Hegde, R.K.; Samarth, Bharadwaj D D; Shreyas, S.; Gowranga, K H

doi:10.1109/rteict.2017.8256856

Cited by 12 publications

(2 citation statements)

References 2 publications

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“…However, it requires the Raspberry Pi board at one's discourse for implementation which is quite costly. Another way of Traffic sign recognition is picture intensive [3]. A video is acquired and broken down into frames.…”

Section: Literature Surveymentioning

confidence: 99%

Traffic Sign Board Recognition and Voice Alert System using Convolutional Neural Network

Prasad¹

2023

IJSREM

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Abstract—To ensure a smooth and secure flow of traffic, road signs are essential. A major cause of road accidents is negligence in viewing the Traffic signboards and interpreting them incorrectly. The proposed system helps in recognizing the Traffic sign and sending a voice alert through the speaker to the driver so that he/ she may take necessary decisions. The proposed system is trained using Convolutional Neural Network (CNN) which helps in traffic sign image recognition and classification. A set of classes are defined and trained on a particular dataset to make it more accurate. The German Traffic Sign Benchmarks Dataset was used, which contains approximately 43 categories and 51,900 images of traffic signs. The accuracy of the execution is about 98.52 percent. Following the detection of the sign by the system, a voice alert is sent through the speaker which notifies the driver. The proposed system also contains a section where the vehicle driver is alerted about the traffic signs in the near proximity which helps them to be aware of what rules to follow on the route. The aim of this system is to ensure the safety of the vehicle's driver, passengers, and pedestrians. Keywords—Convolutional Neural Network, GTSRB Dataset, Object Detection, Object Classification Traffic, Traffic Signs, Voice Alert

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Section: Literature Surveymentioning

confidence: 99%

Traffic Sign Board Recognition and Voice Alert System using Convolutional Neural Network

Prasad¹

2023

IJSREM

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“…They are characterized by many features make them recognizable concerning the environment. Harini S [16] used background subtraction, object labeling and morphological operation for the detection of the red object for pre-processing and component extraction. Template matching using correlating coefficients is applied to recognize the detected object.…”

Section: Literature Surveymentioning

confidence: 99%

A New Area based Algorithm for Traffic Sign Board Detection and Classification by SVM Approach

Chincholkar¹,

Kumar²

2018

IJCA

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This paper proposes a new approach for traffic sign board detection and also describes the SVM approach for the shape recognition into different classes of traffic sign boards. First, the object is detected using area-based analysis. The areabased analysis is performed on the video frames based on the circularity parameter. The test input for the project is video obtained from a camera mounted on a vehicle. Before that, the image pre-processing techniques are applied to improve the quality of the image and to convert it into a binary image. Then the properties of the image frame have been obtained for further analysis. The feature generation is done to get the principal component for the classification of objects. Then the machine learning algorithm is applied to the dataset classification purpose. The classification into different classes (unsupervised shape recognition) is done with the SVM approach.

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IoT Based Traffic Management in Smart Cities

Farheen¹,

Ashwini²,

Priyanka³

et al. 2018

International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018

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A smart driver alert system for vehicle traffic using image detection and recognition technique

Cited by 12 publications

References 2 publications

Traffic Sign Board Recognition and Voice Alert System using Convolutional Neural Network

Traffic Sign Board Recognition and Voice Alert System using Convolutional Neural Network

A New Area based Algorithm for Traffic Sign Board Detection and Classification by SVM Approach

IoT Based Traffic Management in Smart Cities

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