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

ai.cs.uni.dortmund.de

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

1.3. Structure of this work

be useful in the context of video segmentation and tagging video segments. As we can

assume that

1. the amount of possible features extractable from video data is almost infinite and

2. we do initially not know which features are useful

we start by extracting a maximized number of exemplary features. As analyzing the video

stream and creating recommendations is supposed to happen in real-time, the amount of

selected features must not be too large at the end. Especially complex features (in terms

of time needed to calculate them) should be avoided to guarantee real-time behavior.

As a next step the extracted features have to be applied to real-world problems in order

to evaluate, which subset of features is useful. Hereby the identification of features that

appear to be useless is an important result as well, as these features do not have to

be taken into account when later on video streams are analyzed in real-time. In this

thesis two important video segmentation and tagging problems for IP-TV broadcasts

have been considered as use cases (= applications):

1. The segmentation of videos by detecting cuts and gradual transitions, and

2. the tagging of news videos by distinguishing between anchorshots and news report

shots.

There are of course other reasonable segmentation and tagging tasks as well, such as

the identification of advertisement in movies and tagging the segments of a broadcasted

movie with the label ”movie” or ”advertisement”. But as the set of features, that have

to be extracted to solve other tasks, will be similar, I decided to focus on the above

mentioned use cases.

Furthermore a second use case has been built up. This one is closer related to surveillance

applications than to IP-TV, as it is about supervising the coffee consumption for our

office. The learning tasks for both use cases are defined in more detail later on.

Last but not least, we have to take into account that the feature extraction is supposed

to be run in real-time on live video streams. Hence this thesis should demonstrate, how

feature extraction in real-time can be performed and keep an eye on the runtime of

segmentation and tagging algorithms.

1.3. Structure of this work

This work is structured in eight chapters. This Introduction chapter includes a motivation

of the problem and an introduction of the context. The subsequent chapters are

structured as follows:

Chapter 2 provides a literature survey of the field of video segmentation and tagging.

Beside a definition of the terms segmentation and tagging, promising approaches

for different segmentation tasks are introduced.

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