Real-time feature extraction from video stream data for stream ...

Real-time feature extraction from video stream data for stream ...

1. Introduction

• Digital transmission allows better video and sound quality (HDTV).

• The number of potential channels is larger as no additional efforts have to be

undertaken to receive the television programs of foreign stations.

• In many cases recording and video-on-demand functionality is already integrated

and therefore free as well.

But IP-TV has even more advantages beside these obvious ones. The upcoming IP-TV

”has begun to bring consumers (a) new TV experience which goes beyond any traditional

passive TV” [Zeadally et al., 2011]. Reason for this is that IP-TV, contrary to

the traditional TV, provides a feedback channel which allows interactivity with and personalization

of the TV program. By tracking the switching behavior of users over the

feedback channel, IP-TV providers can discover, what ever single customer is watching

and fit the program to the customers’ needs. This includes for instance a personalized

recommendation of shows or an adaption of advertisement to the customers interest.

However not only the customer but also the content providers profit. By fitting advertisement

to the users needs, users are more likely to really buy the advertised products.

Thus the value of the advertisement increases and hence content providers can earn more

money without increasing the amount of advertisement. Furthermore the expensive and

imprecise evaluation of user behavior by questionnaires or tracking the switching behavior

of some random and representative households by using set-top box based data

becomes redundant.

The above mentioned advantages require an extensive analysis of the gained user and

video data. This includes on the one hand the enrichment of the data by all information

available and on the other hand the analysis of this enriched data with machine learning

approaches. As this analysis is as well of high scientific as of economic interest, big

efforts have been made in the past twenty years to cope with the problem. These efforts

include the extraction of features from audio and video data, the automatic parsing, segmenting,

indexing and tagging of video content and the development of recommendation

approaches suitable for video data based on user behavior. As tasks are various, scientists

from many fields of research (computer scientists specialized on vision, machine learning

or recommendation systems, mathematics and psychologists) have contributed and

results are manifold. Nevertheless there is no existing overall system for real-time program

recommendations based on user- and video-data. The new European Union-funded

collaborative research project ViSTA-TV is addressing this problem.

The results of this thesis are supposed to be used within the ViSTA-TV project. By

extracting real-time features from stream video data, we gain further knowledge about

the video stream itself and thereby improve the quality of the data. The idea is, that

the enriched data helps to built a recommendation engine. The next section is going to

provide the reader with some basic information about the project.


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