Date of Original Version
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-32060-6_47
Abstract or Description
Visual object detection in robot soccer is fundamental so the robots can act to accomplish their tasks. Current techniques rely on manually highly polished definitions of object models, that lead to accurate detection, but are quite often computationally inefficient. In this work, we contribute an efficient object detection through regression (ODR) method based on offline training. We build upon the observation that objects in robot soccer are of a well defined color and investigate an offline learning approach to model such objects. ODR consists of two main phases: (i) offline training, where the objects are automatically labeled offline by existing techniques, and (ii) online detection, where a given image is efficiently processed in real-time with the learned models. For each image, ODR determines whether the object is present and provides its position if so. We show comparing results with current techniques for precision and computational load.
Lecture Notes in Computer Science, 7416, 550-561.