Augmented reality in sport scenarios using cameras mounted on a tripod

[ES] In this paper we address the problem of inserting virtual content in a video sequence. The method we propose uses just image information. We perform primitive tracking, camera calibration, real and virtual camera synchronisation and finally rendering to insert the virtual content in the real video sequence. To simplify the calibration step we assume that cameras are mounted on a tripod (which is a common situation in practise). The primitive tracking procedure, which uses lines and circles as primitives, is performed by means of a CART (Classification and Regression Tree). Finally, the virtual and real camera synchronisation and rendering is performed using functions of OpenGL (Open Graphic Library). We have applied the method proposed to sport event scenarios, specifically, soccer matches. In order to illustrate its performance, it has been applied to real HD (High Definition) video sequences. The quality of the proposed method is validated by inserting virtual elements in such HD video sequence.

Fast and accurate hand pose detection for human-robot interaction

[EN]Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.