Pedro Gil-Jiménez

Road-Sign Detection and Recognition Based on Support Vector Machines

This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driver-assistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions

Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition

This paper presents a quantitative comparison of several segmentation methods (including new ones) that have successfully been used in traffic sign recognition. The methods presented can be classified into color-space thresholding, edge detection, and chromatic/achromatic decomposition. Our support vector machine (SVM) segmentation method and speed enhancement using a lookup table (LUT) have also been tested. The best algorithm will be the one that yields the best global results throughout the whole recognition process, which comprises three stages: 1) segmentation; 2) detection; and 3) recognition. Thus, an evaluation method, which consists of applying the entire recognition system to a set of images with at least one traffic sign, is attempted while changing the segmentation method used. This way, it is possible to observe modifications in performance due to the kind of segmentation used. The results lead us to conclude that the best methods are those that are normalized with respect to illumination, such as RGB or Ohta Normalized, and there is no improvement in the use of Hue Saturation Intensity (HSI)-like spaces. In addition, an LUT with a reduction in the less-significant bits, such as that proposed here, improves speed while maintaining quality. SVMs used in color segmentation give good results, but some improvements are needed when applied to achromatic colors.

Traffic sign shape classification evaluation I: SVM using distance to borders

This paper deals with the detection and classification of traffic signs in outdoor environments. The information provided by traffic signs on roads is very important for the safety of drivers. However, in these situations the illumination conditions can not be predicted, the position and the orientation of signs in the scene are not known and other objects can block the vision of them. For these reasons we have developed an extensive test set which includes all kind of signs. In an artificial vision system, the key to recognize traffic signs is how to detect them and identify their geometric shapes. So, in this work we propose a method that uses a technique based on support vector machines (SVMs) for the classification. The patterns generated by the vectors represent the distances to borders (DtB) of the objects candidate to be traffic signs. Experimental results show the effectiveness of the proposed method.

Traffic sign shape classification evaluation. Part II. FFT applied to the signature of blobs

In this paper we have developed a new algorithm of artificial vision oriented to traffic sign shape classification. The classification method basically consists of a series of comparison between the FFT of the signature of a blob and the FFT of the signatures of the reference shapes used in traffic signs. The two major steps of the process are: the segmentation according to the color and the identification of the geometry of the candidate blob using its signature. The most important advances are its robustness against rotation and deformation due to camera projections.

Shape classification algorithm using support vector machines for traffic sign recognition

In this paper, a new algorithm for traffic sign recognition is presented. It is based on a shape detection algorithm that classifies the shape of the content of a sign using the capabilities of a Support Vector Machine (SVM). Basically, the algorithm extracts the shape inside a traffic sign, computes the projection of this shape and classifies it into one of the shapes previously trained with the SVM. The most important advances of the algorithm is its robustness against image rotation and scaling due to camera projections, and its good performance over images with different levels of illumination. This work is part of a traffic sign detection and recognition system, and in this paper we will focus solely on the recognition step.

Traffic sign shape classification based on Support Vector Machines and the FFT of the signature of blobs

In many traffic sign recognition systems, one of the main tasks is the classification of the shape of the blob, which is intended to simplify the recognition process. In this paper, we have developed a new shape classification algorithm based on Support Vector Machines classifiers and the FFT of the signature of the blob. The FFT of the signature yields invariance to object scalings and rotations. Furthermore, the FFT is the vector input to the classifier. This classifier is trained to cope with projection deformations and occlusions. The algorithm has been tested under adverse conditions, such as geometric distortions, i.e. scaling, rotations and projection deformations, and occlusions. The experimental results show good robustness when the system is working with real, outdoor road images.

Automatic Control of Video Surveillance Camera Sabotage

One of the main characteristics of a video surveillance system is its reliability. To this end, it is needed that the images captured by the videocameras are an accurate representation of the scene. Unfortunately, some activities can make the proper operation of the cameras fail, distorting in some way the images which are going to be processed. When these activities are voluntary, they are usually called sabotage, which include partial o total occlusion of the lens, image defocus or change of the field of view. In this paper, we will analyze the different kinds of sabotage that could be done to a video surveillance system, and some algorithms to detect these inconveniences will be developed. The experimental results show good performance in the detection of sabotage situations, while keeping a very low false alarm probability.

Fundaments in Luminance and Retroreflectivity Measurements of Vertical Traffic Signs Using a Color Digital Camera

This paper is a study of the influences of the different parameters which affect the photometric evaluation of light-emitting surfaces (due to reflection or self-emission) when a conventional color digital camera is used. The overall purpose of this paper is to evaluate the luminance and the reflectivity of the vertical traffic sign with the camera in order to provide an automatic recognition of deteriorated reflective sheeting material of which the traffic signs were made. This paper describes how the A/D converter output signal given by a pixel of the digital camera can be related to the luminance and the reflectivity of the corresponding surface element whose image is formed on a pixel. Thus, each surface element of the traffic signs surface can be separately evaluated. By photometrically calibrating the camera, we have been able to prove this relationship in our experiments.

A Tracking System for Automated Inventory of Road Signs

This paper describes the tracking stage of a road sign identification system with the use of a Kalman filter estimator. Although traditionally traffic sign recognition systems work in a single-frame way, tracking using inter-frame information is crucial in order to reduce false alarms. In applications that involve maintenance systems, the purpose of tracking is to give a single stroke of each road sign although it is presented in several frames of a sequence. The tracking module uses the output of the recognition stage, which processes each frame independently. Each traffic sign is followed for a specific distance which is computed in each case based on the system parameters. This setup has permitted improve the accuracy of the performance with respect to the obtained at the output of the recognition system. Additionally most false alarms are eliminated because their characteristic are temporally incoherent.

Background pixel classification for motion detection in video image sequences

Simoncelli & Heeger studied how the motion is processed in humans (V1 and MT areas) and proposed a model based on neural populations that extract the local motion structure through local competition of MT like cells. In this paper we present a neural structure that works as dynamic filter on the top of this MT layer and can take advantage of the neural population coding that is supposed to be present in the MT cortical processing areas. The test bed application addressed in this work is an automatic watch up system for the rear-view mirror blind spot. The segmentation of overtaking cars in this scenario can take full advantage of the motion structure of the visual field provided that the ego-motion of the host car induces a global motion pattern whereas an overtaking car produces a motion pattern highly contrasted with this global ego-motion field.