Recent studies have estimated that television and related equipment account for 1.8% of global greenhouse gas (GHG) emissions and Information and Communication Technology is responsible for 2% of global GHG emissions. Both these sectors are forecast to grow as the developing world increases its uptake of technology.This study estimates the carbon footprint of two different ways of watching television: using broadcast digital terrestrial television (DTT) and video-ondemand (VOD) over the Internet. It compares the two distribution methods and the corresponding consumer equipment. It uses the principles of life cycle assessment (LCA) to derive the carbon footprints using a bottom-up analysis of the system applied to the BBC’s television services. This was the only environmental impact considered and was mainly from electricity use. Equipment manufacturing was not included.

In order to insert a virtual object into a TV image, the graphics system needs to know precisely how the camera is moving, so that the virtual object can be rendered in the correct place in every frame. Nowadays this can be achieved relatively easily in postproduction, or in a studio equipped with a special tracking system. However, for live shooting on location, or in a studio that is not specially equipped, installing such a system can be difficult or uneconomic. To overcome these limitations, the MATRIS project is developing a real-time system for measuring the movement of a camera. The system uses image analysis to track naturally occurring features in the scene, and data from an inertial sensor. No additional sensors, special markers, or camera mounts are required. This paper gives an overview of the system and presents some results.

Accurate acquisition of camera position and orientation is crucial for realistic augmentations of camera images. Computer vision based tracking algorithms, using the camera itself as sensor, are known to be very accurate but also time-consuming. The integration of inertial sensor data provides a camera pose update at 100 Hz and therefore stability and robustness against rapid motion and occlusion. Using inertial measurements we obtain a precise real time augmentation with reduced camera sample rate, which makes it usable for mobile AR and See-Through applications.This paper presents a flexible run-time system, that benefits from sensor fusion using Kalman filtering for pose estimation. The camera as main sensor is aided by an inertial measurement unit (IMU). The system presented here provides an autonomous initialisation as well as a predictive tracking procedure and switches between both after successfull (re)-initialisation and tracking failure respectively. The computer vision part performs 3D model-based tracking of natural features using different approaches for yielding both, high accuracy and robustness. Results on real and synthetic sequences show how inertial measurements improve the tracking.