LIVE: An Integrated Production and Feedback System for Intelligent and Interactive TV Broadcasting

Abstract

In this paper, we report recent research activities under the integrated project, Live Staging of Media Events (LIVE), which is funded under European Framework-6 programme, and illustrate how a new LIVE TV broadcasting and content production concept can be introduced to improve the existing TV broadcasting services. In comparison with existing TV content production technologies, we show case that LIVE TV broadcasting format could achieve a range of significant advantages, which can be highlighted as: (i) real-time interaction between TV content production team and viewers to ensure the best possible entertainment experiences and to allow viewers not only to view but also to participate, to influence and to control; (ii) the traditional concept of TV content director can be changed to a TV content conductor, where live TV broadcasting can be conducted on real-time bases and existing content materials can be conducted into multi-streams of TV broadcasting, providing variety of choices to allow the audience to make their own preferences in watching TV programmes; (iii) introduction of significant intelligence in terms of content analysis and behaviour analysis etc. to further improve the quality of services based on the fundamental concept of LIVE project. When tested under field trials for 2008 Olympic Games by ORF, the developed LIVE system has illustrated great potential as well as significant promises to improve the TV broadcasting services not only in terms of the interaction formats, but also in terms of viewing experiences as well as the style of content productions and preparations Index Terms-Interactive TV, Intelligent TV, and semantics-based video processing.

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

“…In our future work, we will investigate how to optimally determine the value of D based on the image content and further improve the detection speed and accuracy. In addition to image-splicing detection-by trace, integration of additional most state-of-the-art feature extraction approaches such as sub-pixel image matching (Jiang et al 2011;Ren et al 2010), sparse representation (Zhao et al 2013), saliency detection and deep learning (Han et al 2015a;Han et al 2015b) as well as singular spectrum analysis et al Zabalza et al 2014) will also be focused for future study. Detection results of the SIFT scheme and proposed scheme.…”

A Secure Communication Model with Compressive-Sensed Multimedia Data

“…Future work will be in several ways. One is video analysis before transcoding and transmission, including video segmentation and content extraction [19][20][21] as well as denoising and decomposition [22][23][24]. In addition, object-based analysis with most state-of-the-art machine learning approaches will be highlighted as well, using deep learning and weakly-supervised learning [25][26][27].…”

Reliable camera motion estimation from compressed MPEG videos using machine learning approach

“…In Ren et al [3], camera motion is estimated from MPEG videos for event based video indexing and retrieval. In Jiang et al [4], the extracted camera motion are applied in detecting combined video events such as closing up of players in sports videos. In Ren et al [5], camera motion is estimated as global motion by using phase correlation and then applied to compensate frame difference for the detection of film dirt in archive restoration applications.…”

“…Since compressed-domain processing can avoid time-consuming fully decoding of the video, camera motion estimation from compressed videos is preferred [1,3,4,6,7].…”

“…Among these approaches, the classic work in [1] needs particular attention as it has successfully directly motivated several other approaches [3, 4,6].…”