• Pallvee Bhairam
• Dr.Gargi Shankar Verma

The Traffic Sign Detection and Recognition System (TSDRS), which helps moving vehicles perceive and comprehend traffic signs, is essential for improving road safety. In order to recognize and identify traffic signals, this study introduces a novel method that combines LBP and HOG characteristics. Local textures are robustly represented by LBP, Potential zones of interest are found by analyzing the LBP patterns within the sliding panes, suggesting the existence of traffic signs. Subsequently. using the HOG descriptor, shape and edge characteristics are extracted from the identified regions of interest. The HOG descriptor is well known for its effectiveness in capturing object shapes and gradients. The discovered candidates are then classified using the derived HOG features. For this purpose, we are using a trained classifier such as a Support Vector Machine (SVM). Based on all retrieved HOG characteristics, the classifier can discriminate between different kinds of traffic sign. Experimental findings show that the suggested strategy is effective.

TSD, color and shape segmentation, thresholding; TS recognition, feature extraction HOG, LBP; PCA; SVM.

"System Using LBP HOG to Detect and Recognize Traffic Sign", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.f223-f227, September-2023, Available :http://www.jetir.org/papers/JETIR2309527.pdf

"System Using LBP HOG to Detect and Recognize Traffic Sign", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppf223-f227, September-2023, Available at : http://www.jetir.org/papers/JETIR2309527.pdf