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

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

1. Introduction

and user-data the IP-TV-Providers provide the opportunity to receive EPG (Electronic

Program Guide)-data for the video data as well.

Figure 1.2.: Overview of the ViSTA-TV project

Afterwards the data is enriched with internal and external features. This is done by the

module ”Data Enrichment Engine”. The term internal features refers to all features that

are extracted from the provided data itself. Examples for this are any features gathered

from the video data. In contrast to this, external features are all context information

taken from third party content providers such as DBpedia 4 or Discogs 5 . This module is

mainly driven by TU Dortmund University. Its output is an enriched feature stream, enabling

the following modules to do further market analysis and create recommendations

for users. This is done by the ”Recommendation Engine” module. The ”Data Warehouse”

module is responsible for building up the infrastructure to store and deliver the

enriched data.

Parts of the topic of this diploma thesis are supposed to be used in the ”Data Enrichment”

workpackage of the ViSTA-TV project.

1.2. Task of this thesis

Part of the ViSTA-TV project is to analyze, whether there are observable events within

the video, audio or text streams that make people switch channels, and combine this

with external features such as peoples’ interests or peoples’ activities on social media

platforms. This requires the identification of video events. Identifying video events again

can be seen as the problem of segmenting a video stream in meaningful parts and tagging

these parts. This again requires the extraction and evaluation of features, that might




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