Improving the Classification Accuracy of Accurate Traffic Sign Detection and Recognition System Using HOG and LBP Features and PCA-Based Dimension Reduction

Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve high recognition accuracy. However, these methods first require a large number of carefully crafted traffic sign datasets for the training process. Moreover, since traffic signs differ in each country and there is a variety of traffic signs, these methods need to be fine-tuned when recognizing new traffic sign categories. To address these issues, we propose a traffic sign matching method for zero-shot recognition. Our proposed method can perform traffic sign recognition without training data by directly matching the similarity of target and template traffic sign images. Our method uses the midlevel features of CNNs to obtain robust feature representations of traffic signs without additional training or fine-tuning. We discovered that midlevel features improve the accuracy of zero-shot traffic sign recognition. The proposed method achieves promising recognition results on the German Traffic Sign Recognition Benchmark open dataset and a real-world dataset taken from Sapporo City, Japan.

Section: Traditional Traffic Sign Recognition Methodsmentioning

confidence: 99%

Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching

Gan,

Li,

Togo

et al. 2023

“…Another study by Soni et al, (2019) [2] used HOG and LBP descriptors with the Principal Component Analysis (PCA) and Support Vector Machines (SVM) to classify traffic signs. The study used the Chinese Traffic Sign Database (TSRD) with 58 classes and 6164 images, and the best performing method was the LBP with the PCA and SVM classifiers, achieving an accuracy level of 84.44%.…”

Section: Machine Learning For Traffic Sign Recognitionmentioning

confidence: 99%

Recent Advances in Traffic Sign Recognition: Approaches and Datasets

Lim

Lee

Lim

et al. 2023

Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.

“…Feature extractors and supervised classifiers have been arranged differently to minimize misclassifications in a wide variety of domains. Soni et al [ 24 ] processed the Chinese traffic sign dataset through SVM, trained on the HOG or LBP after Principal Component Analysis (PCA), reaching an accuracy of 84.44%. A similar setup was used by Manisha and Liyanage [ 21 ], who achieved 98.6% accuracy on vehicles moving at 40–45 km/h.…”

Section: Background On Traffic Sign Recognitionmentioning

confidence: 99%

“…Throughout the years, many studies tackled TSR [ 21 , 22 , 23 ] using different feature descriptors and ML-based classifiers. Different combinations of such classifiers and features have been proven to generate heterogeneous classification scores [ 15 , 19 , 24 , 25 , 26 , 27 ] motivating the need for comparisons to discover the optimal classifier for a given TSR problem [ 3 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Atif¹,

Zoppi²,

Gharib

et al. 2022

Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR.