Abstract-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 driverassistance 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.

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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 2 . In this way, the filter bank performance can be significantly improved compared to other existing design methods.

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a b s t r a c tIn 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.

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