Loading...
Please wait, while we are loading the content...
Similar Documents
Traffic sign recognition using SVM and convolutional neural network
| Content Provider | Scilit |
|---|---|
| Author | Ayyajjanavar, Renuka B. Jayarekha, P. |
| Copyright Year | 2021 |
| Description | Traffic Sign Recognition (TSR) is one of the most popular topics in image processing, which uses machine learning and deep learning techniques to identify the signs. TSR is an important feature of Advance Driver Assistant System (ADAS), which provides necessary information about the road. This helps to overcome road accidents and provides safety to drivers. This research gives implementation for TSR using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithm. Here, we are applying these two algorithms on German Traffic Sign Recognition Dataset (GTSRD) and computing the accuracy for both algorithms along with train time and prediction time. Then by analyzing the performance, we are suggesting the suitable algorithm for TSR. Book Name: Smart Computing |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003167488-58&type=chapterpdf |
| Ending Page | 492 |
| Page Count | 9 |
| Starting Page | 484 |
| DOI | 10.1201/9781003167488-58 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-06-18 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Smart Computing Hardware and Architecturee Neural Road Tsr Traffic Sign Recognition Driver Safety |
| Content Type | Text |
| Resource Type | Chapter |
