A Real-Time Ball Detection Approach Using Convolutional Neural Networks

Ball detection is one of the most important tasks in the context of soccer-playing robots. The ball is a small moving object which can be blurred and occluded in many situations. Several neural network based methods with different architectures are proposed to deal with the ball detection. However, they are either neglecting to consider the computationally low resources of humanoid robots or highly depend on manually-tuned heuristic methods to extract the ball candidates. In this paper, we propose a new ball detection method for low-cost humanoid robots that can detect most soccer balls with a high accuracy rate of up to 97.17%. The proposed method is divided into two steps. First, some coarse regions that may contain a full ball are extracted using an iterative method employing an efficient integral image based feature. Then they are fed to a light-weight convolutional neural network to finalize the bounding box of a ball. We have evaluated the proposed approach using a comprehensive dataset and the experimental results show the efficiency of our method

Real-Time Object Recognition using a Multi-Framed Temporal Approach

Computer Vision involves the extraction of data from images that are analyzed in order to provide information crucial to many modern technologies. Object recognition has proven to be a difficult task and programming reliable object recognition remains elusive. Image processing is computationally intensive and this issue is amplified on mobile platforms with processor restrictions. The real-time constraints demanded by robotic soccer in RoboCup competition serve as an ideal format to test programming that seeks to overcome these challenges. This paper presents a method for ball recognition by analyzing the movement of the ball. Major findings include enhanced ball discrimination by replacing the analysis of static images with absolute change in brightness in conjunction with the classification of apparent motion change

Problems and solutions in middle size robot soccer: a review

A review of current scientific and technological problems encountered in building and programming middle size soccer robots is made in this paper. Solutions and solution trends to the problems, as presented by different teams, are also examined. Perceptual systems of individual robots, in particular with respect to object location, communications between robot players, decision making with regard to game strategy and behaviour generation, and, finally, actuation, are the topics dealt with. This makes for a wide perspective on the actual state of the art of middle size soccer robots

Object detection in robotics using morphological information

Mestrado em Engenharia Electrónica e TelecomunicaçõesUma das componentes mais importantes em sistemas de processamento de imagem é a detecção de objectos de interesse. Contudo, a detecção de objectos é um desafio. Dada uma imagem arbitrária e assumindo que se está interessado em localizar um determinado objecto, o grande objectivo da detecção de objectos passa por determinar se existe ou não qualquer objecto de interesse. Esta tese encontra-se inserida no domínio do RoboCup e foca o desenvolvimento de algoritmos para a detecção de bolas oficiais da FIFA, um objecto importante no futebol robótico. Para atingir o objectivo principal, foram desenvolvidos três algoritmos para detectar bolas de futebol com cores arbitrárias, usando informação morfológica obtida através do detector de cortornos Canny e da tranformada de Hough. Em primeiro lugar, foi desenvolvida uma abordagem onde se implementou um algoritmo específico usando a transformada de Hough circular. Em segundo lugar, foi implementado um algoritmo que utiliza uma função da biblioteca OpenCV dedicada à procura de círculos em imagens. Finalmente, os dois primeiros algoritmos foram agrupados para criar uma nova abordagem, na qual ambos os algoritmos são usados. São apresentados resultados experimentais que mostram que os algoritmos desenvolvidos são precisos, sendo capazes de realizar a detecção da bola de forma confiável em situações de tempo-real. ABSTRACT: One of the most important steps in image processing systems is the detection of objects of interest. However, object detection is a challenging task. Given an arbitrary image and assuming that we are interested in locating a particular object, the goal of object detection is to determine whether or not there is any object of interest. This thesis is inserted in the RoboCup domain and is focused on the development of algorithms for the detection of arbitrary FIFA balls, an important object for soccer robots. To achieve the main objective, we developed three algorithms to detect arbitrary soccer balls using morphological information given by the Canny edge detector and the Hough Transform. First, it was developed an approach where we implemented a specific algorithm using the circular Hough Transform, applied after the segmentation of the acquired image. Secondly, it was implemented an algorithm that uses a function of the OpenCV library dedicated to the search of circles in images. Finally, the two first algorithms were joined to create a new approach in which both of the algorithms are used. Experimental results are presented, showing that the developed algorithms are accurate, being capable of reliable ball detection in real-time situations