In-vehicle camera traffic sign detection and recognition

Abstract

In this paper we discuss theoretical foundations and a practical realization of a real-time traffic sign detection, tracking and recognition system operating on board of a vehicle. In the proposed framework a generic detector refinement procedure based on a mean shift clustering is introduced. This technique is shown to improve the detection accuracy and reduce the number of false positives for a broad class of object detectors for which a soft response's confidence can be sensibly measured. Track of an already established candidate is maintained over time using an instance-specific tracking function that encodes the relationship between a unique feature representation of the target object and the affine distortions it is subject to. We show that this function can be learned on-the-fly via regression from random transformations applied to the image of the object in known pose. Secondly, we demonstrate its capability of reconstructing the full-face view of a sign from substantial viewangles. In the classification stage a concept of a similarity measure learned from image pairs is discussed and its realization using SimBoost, a novel version of AdaBoost algorithm, is analyzed. Suitability of the proposed method for solving multi-class traffic sign classification problems is shown experimentally for different image representations. Overall performance of the entire system is evaluated based on a prototype C++ implementation. Illustrative output generated by this demo application is provided as a supplementary material attached to this paper. Journal of Machine Vision & ApplicationsThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. In-Vehicle Camera Traffic Sign Detection and RecognitionAndrzej Ruta · Fatih Porikli · Yongmin Li · Shintaro WatanabeReceived: dd-mm-yyyy / Accepted: dd-mm-yyyyAbstract In this paper we discuss theoretical foundations and a practical realization of a real-time traffic sign detection, tracking and recognition system operating on board of a vehicle. In the proposed framework a generic detector refinement procedure based on a mean shift clustering is introduced. This technique is shown to improve the detection accuracy and reduce the number of false positives for a broad class of object detectors for which a soft response's confidence can be sensibly measured. Track of an already established candidate is maintained over time using an instance-specific tracking fun...

A system for large-scale automatic traffic sign recognition and mapping

“…First stages of our cascade consist of AdaBoost classifiers trained on dissociated dipoles features (Balas et al, 2003) that were also used in (Baro et al, 2009). We preprocess sign images as described in (Ruta et al, 2011) with filters that intend to amplify certain colors and suppress any others (for example, we amplify blue color in the case of blue squares sign type). On each stage we use for training 10000 synthetic samples of signs and 16000 background patches bootstrapped from real images.…”

“…First cascade was trained on grayscale images, another cascade on images with amplified blue color as in (Ruta et al, 2011). It is clear from Figure 3 and Figure 4 that color features are doing better job in terms of accuracy and the number of features.…”

Traffic sign detection and analysis: Recent studies and emerging trends

“…In this paper, we have chosen to cover 4 recent leading papers [7], [9], [10], [11] that describe different methods of detecting signs. These papers, apart from being very recent, cover trends in the area well: Some use theoretical sign models, some use learned models, some are mainly colorbased, some rely more on shapes, some have extensive focus on tracking.…”

“…A. Segmentation [9] opts to use a color based segmentation. They propose a quad-tree attention operator.…”

Détection des panneaux de signalisation routière par accumulation bivariée

“…Les performances en généralisation des modèles d'apparence sont limitées par la variété des exemples de la base d'apprentissage. Ainsi, (Ruta et al, 2009b) comparent la détection de panneaux circulaires par transformée de Hough à celle obtenue par une cascade de classifieurs boostés et sélectionnent la transformée de Hough pour sa flexibilité et sa rapidité.…”