Traffic Sign Detection and Recognition Model Using Support Vector Machine and Histogram of Oriented Gradient

Abstract

Traffic signs are symbols erected on the sides of roads that convey the road instructions to its users. These signs are essential in conveying the instructions related to the movement of traffic in the streets. Automation of driving is essential for efficient navigation free of human errors, which could otherwise lead to accidents and disorganized movement of vehicles in the streets. Traffic sign detection systems provide an important contribution to automation of driving, by helping in efficient navigation through relaying traffic sign instructions to the system users. However, most of the existing techniques have proposed approaches that are mostly capable of detection through static images only. Moreover, to the best of the author’s knowledge, there exists no approach that uses video frames. Therefore, this article proposes a unique automated approach for detection and recognition of Bangladeshi traffic signs from the video frames using Support Vector Machine and Histogram of Oriented Gradient. This system would be immensely useful in the implementation of automated driving systems in Bangladeshi streets. By detecting and recognizing the traffic signs in the streets, the automated driving systems in Bangladesh will be able to effectively navigate the streets. This approach classifies the Bangladeshi traffic signs using Support Vector Machine classifier on the basis of Histogram of Oriented Gradient property. Through image processing techniques such as binarization, contour detection and identifying similarity to circle etc., this article also proposes the actual detection mechanism of traffic signs from the video frames. The proposed approach detects and recognizes traffic signs with 100% precision, 95.83% recall and 96.15% accuracy after running it on 78 Bangladeshi traffic sign videos, which comprise 6 different kinds of Bangladeshi traffic signs. In addition, a public dataset for Bangladeshi traffic signs has been created that can be used for other research purposes.

BanTrafficNet: Bangladeshi Traffic Sign Recognition Using A Lightweight Deep Learning Approach

“…Very few researchers focus on Bangladeshi traffic sign recognition. Among them, Ahmed et al 9 present a robust Traffic Sign Detection and Recognition model utilizing a Support Vector Machine and Histogram of Oriented Gradient, achieving impressive 3/21 results of 100% precision, 95.83% recall, and 96.15% accuracy when tested on 78 Bangladeshi traffic sign videos. A public dataset for Bangladeshi traffic signs has been developed, contributing to the research community and real-world applications.…”

“…In contrast to traditional Machine Learning (ML) methods such as SVM and random forest, which have yielded poor accuracy in the context of Bangladeshi traffic sign recognition 9 , our approach harnesses the power of lightweight CNNs to achieve significantly improved accuracy. While some researchers have explored deep learning pre-trained models, which often come with high computational costs, we have focused on creating an efficient, tailored solution 10 .…”

Nonlinear Rail Accessibility and Road Spatial Pattern Effects on House Prices

“…Therefore, the discussion of nonlinear models can also be extended to more discussions on price research [47]. In addition, transportation factors are affecting our lives in a more diverse and subtle way, so other scholars are called on to explore the development of transportation systems from the perspective of sustainable optimization in the future [48].…”

Road Sign Recognition Method Based on Segmentation and Attention Mechanism

“…Finally, the structure-matching algorithm was used to test whether the subset of matched interest point pairs formed road signs matching the road signs in the die plate image. Ahmed et al [19] used the improved Hu invariant moment to construct the feature vector of the image and used THE SVM classifier to classify three typical road traffic signs, including straight-ahead signs, straight-right turn signs, and left-right turn signs, but the recognition categories were few and real-time detection could not be achieved.…”