A robust algorithm for detection and classification of traffic signs in video data

Abstract

Abstract-The accurate identification and recognition of the traffic signs is a challenging problem as the developed systems have to address a large number of imaging problems such as motion artifacts, various weather conditions, shadows and partial occlusion, issues that are often encountered in video traffic sequences that are captured from a moving vehicle. These factors substantially degrade the performance of the existing traffic sign recognition (TSR) systems and in this paper we detail the implementation of a new strategy that entails three distinct computational stages. The first component addresses the robust identification of the candidate traffic signs in each frame of the video sequence. The second component discards the traffic sign candidates that do not comply with stringent shape constraints, and the last component implements the classification of the traffic signs using Support Vector Machines (SVMs). The main novel elements of our TSR algorithm are given by the approach that has been developed for traffic sign classification and by the experimental evaluation that was employed to identify the optimal image attributes that are able to maximize the traffic sign classification performance. The TSR algorithm has been validated using video sequences that include the most important categories of signs that are used to regulate the traffic on the Irish and UK roads, and it achieved 87.6% sign detection, 99.2% traffic sign classification accuracy and 86.7% overall traffic sign recognition.

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

“…More precisely, SVM is a binary classifier that separates two different classes by a subset of data samples called support vectors. It was implemented as a classifier for traffic sign recognition in [44,55,88,129,130,131,132,133,134,135,136]. This classification method is robust, highly accurate and extremely fast which is a good choice for large amounts of training data.…”

Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method

“…denote a set of all CtC distances computed by (5). Define a positive integer number D that determines the dimension of the proposed CtC descriptor.…”

“…In [4], Paulo and Correia proposed to analyse red and blue colour information contained on the image before classifying the detected road signs using their shape characteristics. In [5], Bui‐Ninh et al proposed to convert RGB images to HSV or HSI colour representations. The road sign was then detected by segmenting the Hue component as it shows more robustness against variations in light and weather conditions.…”

Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods

“…Carrasco et al [12] execute a comparison between two methods used in the past for detection and recognition of road signs: Template matching and feed-forward neural networks while neural networks are also exploited by Miyata [13] for speed limit numbers recognition using an eigen space method and color features. Bose et al [14] focus on enhanced dual-band spectral analysis in the hue-saturation-intensity (HSI) and RGB (Red Green Blue) domains while Marmo et al [15], in 2006, enhanced identification of rectangular signs through the optical flow and Hough transform; Nguwi and Kouzani [16] present classification methods applied to road sign recognition divided into color-based, shape-based, and others, while Bui-Minh, Ghita et al [17,18] face video detection and object occlusions.…”