Algorithms for classifying road signs have a high computational cost per pixel processed. A detection stage that has a lower computational cost can facilitate real-time processing. Various authors have used shape and color-based detectors. Shape-based detectors have an advantage under variable lighting conditions and sign deterioration that, although the apparent color may change, the shape is preserved. In this paper, we present the radial symmetry detector for detecting speed signs. We evaluate the detector itself in a system that is mounted within a road vehicle. We also evaluate its performance that is integrated with classification over a series of sequences from roads around Canberra and demonstrate it while running online in our road vehicle. We show that it can detect signs with high reliability in real time. We examine the internal parameters of the algorithm to adapt it to road sign detection. We demonstrate the stability of the system under the variation of these parameters and show computational speed gains through their tuning. The detector is demonstrated to work under a wide variety of visual conditions.

Real-Time Speed Sign Detection Using the Radial Symmetry Detector

An automatic road sign recognition system based on a computational model of human recognition processing

We propose a computational model motivated by human cognitive processes for detecting changes of driving environments. The model, called dynamic visual model, consists of three major components: sensory, perceptual, and conceptual components. The proposed model is used as the underlying framework in which a system for detecting and recognizing road signs is developed.

/pdf/a-road-sign-recognition-system-based-on-dynamic-visual-model-4qsnul5908.pdf

A road sign recognition system based on dynamic visual model

This paper presents a noise-tolerant traffic sign recognition method by using both color and shape attributes. First a discriminant analysis is carried out to obtain the color coordinate system giving the best separation between traffic signs and other objects in the scene. Then a recognition algorithm is devised by cascading three modules: an ART2 neural network module to perform color segmentation, a log-polar-exponential grid and Fourier transformation module to extract invariant traffic sign signatures, a backpropagation neural network module to classify such signatures. The performance of this method is evaluated by examining the effect of various noise sources, which may occur in actual outdoor scenes, on the recognition rate. The results obtained indicate the noise-tolerance of the developed methodology.

Traffic sign recognition in noisy outdoor scenes

Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for automated intelligent driving vehicle or driver assistance systems. This paper presents a study to recognize traffic sign patterns using Neural Networks technique. The images are pre-processed with several image processing techniques, such as, threshold techniques, Gaussian filter, Canny edge detection, Contour and Fit Ellipse. Then, the Neural Networks stages are performed to recognize the traffic sign patterns. The system is trained and validated to find the best network architecture. The experimental results show the highly accurate classifications of traffic sign patterns with complex background images and the computational cost of the proposed method.

http://bartlab.org/dr. jackrit's papers/ney/3.krs036_final_submission.pdf

Traffic Sign Recognition for Intelligent Vehicle/Driver Assistance System Using Neural Network on OpenCV

The automatic interpretation of conventional traffic signs is very complex and time consuming. The paper concerns an automatic warning system for driving assistance. It does not interpret the standard traffic signs on the roadside; the proposal is to incorporate into the existing signs another type of traffic sign whose information will be more easily interpreted by a processor. The type of information to be added is profuse and therefore the most important object is the robustness of the system. The basic proposal of this new philosophy is that the co-pilot system for automatic warning and driving assistance can interpret with greater ease the information contained in the new sign, whilst the human driver only has to interpret the "classic" sign. One of the codings that has been tested with good results and which seems to us easy to implement is that which has a rectangular shape and 4 vertical bars of different colours. The size of these signs is equivalent to the size of the conventional signs (approximately 0.4 m/sup 2/). The colour information from the sign can be easily interpreted by the proposed processor and the interpretation is much easier and quicker than the information shown by the pictographs of the classic signs.

/pdf/a-new-approach-to-real-time-traffic-sign-recognition-based-3c42cv47ft.pdf

L. Pacheco

Papers

A new approach to real time traffic sign recognition based on colour information