Specialized ensemble of classifiers for traffic sign recognition

Proceeding of: 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastían, España, junio, 2007.Several complex problems have to be solved in order to build Advanced Driving Assistance Systems. Among them, an important problem is the detection and classification of traffic signs, which can appear at any position within a captured image. This paper describes a system that employs independent modules to classify several prohibition road signs. Combining the predictions made by the set of classifiers, a unique final classification is achieved. To reduce the computational complexity and to achieve a real-time system, a previous input feature selection is performed. Experimental evaluation confirms that using this feature selection allows a significant input data reduction without an important loss of output accuracy.The research reported here has been supported by the Ministry of Education and Science under project TRA2004-07441-C03-C02

A new artificial neural network ensemble based on feature selection and class recoding

Many of the studies related to supervised learning have focused on the resolution of multiclass problems. A standard technique used to resolve these problems is to decompose the original multiclass problem into multiple binary problems. In this paper, we propose a new learning model applicable to multi-class domains in which the examples are described by a large number of features. The proposed model is an Artificial Neural Network ensemble in which the base learners are composed by the union of a binary classifier and a multiclass classifier. To analyze the viability and quality of this system, it will be validated in two real domains: traffic sign recognition and hand-written digit recognition. Experimental results show that our model is at least as accurate as other methods reported in the bibliography but has a considerable advantage respecting size, computational complexity, and running tim

Reducing the amount of input data in traffic sign classification

Proceeding of: 3th International Conference Modeling Decisions for Artificial Intelligence, MDAI 2006. Tarragona, Catalonia, Spain, april 3-5th, 2006.Several complex problems have to be solved in order to build Intelligent Transport Systems. Among them, it is worth mentioning the detection and classification of tra±c signs which could appear at any position within a captured image. This paper analyzes the influence of the number of attributes in the field of classification of tra±c signs when automatic learning techniques are used. In order to face this task, four different approaches have been considered, three of them symbolic and one sub-symbolic. These techniques have been applied using two different input pattern dimensions and their performances have been compared.The research reported here was carried out as a part of the research project CICYT TRA2004-07441-C03-01.No publicad

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio