Saturnino Maldonado-Bascón

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.

An efficient and simple method for designing prototype filters for cosine-modulated pseudo-QMF banks

We present a new method to design prototype filters for conventional cosine-modulated pseudo-quadrature mirror filter (QMF) banks. This method is based on windowing, and sets the 3-dB cutoff frequency of the filter obtained at /spl pi//2M. In this way, the filter bank performance can be significantly improved compared to other existing design methods.

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.

Real-time human segmentation in infrared videos

In this paper, a new approach to real-time people segmentation through processing images captured by an infrared camera is introduced. The approach starts detecting human candidate blobs processed through traditional image thresholding techniques. Afterwards, the blobs are refined with the objective of validating the content of each blob. The question to be solved is if each blob contains one single human candidate or more than one. If the blob contains more than one possible human, the blob is divided to fit each new candidate in height and width.

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.

SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition

This paper proposes a novel approach to recognize object categories in point clouds. By quantizing 3D SURF local descriptors, computed on partial 3D shapes extracted from the point clouds, a vocabulary of 3D visual words is generated. Using this codebook, we build a Bag-of-Words representation in 3D, which is used in conjunction with a SVM classification machinery. We also introduce the 3D Spatial Pyramid Matching Kernel, which works by partitioning a working volume into fine sub-volumes, and computing a hierarchical weighted sum of histogram intersections at each level of the pyramid structure. With the aim of increasing both the classification accuracy and the computational efficiency of the kernel, we propose selective hierarchical volume decomposition strategies, based on representative and discriminative (sub-)volume selection processes, which drastically reduce the pyramid to consider. Results on the challenging large-scale RGB-D object dataset show that our kernels significantly outperform the state-of-the-art results by using a single 3D shape feature type extracted from individual depth images.

Traffic sign recognition system for inventory purposes

This paper describes the evaluation of the characteristics of a real automatic traffic sign detection system. The objective of this review is to provide the basis of quality of a whole system, which is capable of identifying the different signs that can be found in route. At the moment, our work is concerned with the developing of an inventory system capable to get a complete catalog of all the traffic signs and their corresponding state information. The paper analyzes exhaustively the different problems that can appear in real environments and shows how the system implemented overcomes all these difficulties with a high success. The flexibility of the system allows it to run new algorithms even though several of them can be run in parallel and, on the other hand, it is relatively easy to change the training traffic sign according to the circumstances: urban or non-urban environments and traffic signs from different countries.

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.

Spatial-Range Mean-Shift Filtering and Segmentation Applied to SAR Images

The mean-shift (MS) algorithm is applied for reducing speckle noise and segmenting synthetic aperture radar (SAR) images. Two coastal images acquired by Envisats advanced SAR (ASAR) [European Space Agency (ESA)] are used. Studies of the MS parameters are carried out according to the desired product: a speckle filtered image where textures and edges are preserved, or a segmented image, where land and sea are distinguished, as a previous stage for obtaining a land mask and detecting the coastal line. In all cases, Gaussian kernels are used. Speckle filtering results are compared with those obtained using uniform kernels, proving that the former provides better results than the latter. A segmentation approach based on the positions and frequencies at which the MS converges is applied. The use of a combined spatial-range processing and the corresponding bandwidths makes the MS suitable for the two proposed problems. The solid theoretical basis of this procedure allows designing a guided search of the best parameters according to the desired solution, avoiding a tedious trial-and-error process. Although the used images have different characteristics, results prove that similar sets of parameters can be used, showing some degree of robustness with respect to the image, for a given sensor and image acquisition mode.