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

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

4. Learning tasks

of video data, in particular news shows, into shots and stories as one use case for this

thesis. My decision to focus on news videos is due to the fact, that ”news videos receive

greater attention by the scientific community than all the other possible sources of

video” [De Santo et al., 2007]. This interest is mainly due to the fact that broadcasting

companies produce a great amount of video material and the reuse of the material is

often useful. Hence a good segmentation of news video data is enormously important, as

it is the basic step for building up a digital database.The learning tasks are accordingly

defined as follows:

• A1: Identify all shot boundaries in an incoming news stream. As a measure for

success I have chosen the recognition rate, given by precision and recall, as appropriate

evaluation criteria. ”The recognition rate is the most used quality criterion

in order to compare shot change detection methods.” [Smoliar and Zhang, 1994].

• A2: Recognize all anchorshots. Where does one news story end and the next starts?

The success of an inferred classifier can be measured by its’ accuracy.

• A3: Tag all found segments with meaningful keyword. Even if a keyword is valid

for a whole segment, it gets assigned to every single frame included in that segment.

Again the accuracy of the assigned keywords can be taken as an evaluation


I hope that this level of granularity enables us to find explanations for program switches

of users and will hence be useful for the further progress of the ViSTA-TV project.

4.2. Use case B: Coffee - Project

Beside IP-TV, surveillance cameras, traffic supervision cameras and web cams do also

produce massive amount of video data, that has to be analyzed in real-time. As this thesis

is not exclusively limited to IP-TV, I decided to take a second use case into account: Our

office is equipped with a Nespresso coffee maker. This machine uses differently colored

coffee capsules to produce coffee with different flavors. Unfortunately, the consumption

of coffee flavors in our office varies a lot, which makes the reordering of coffee capsules

difficult. Someone has to count the left over coffee capsules first and estimate the flavors

that are missing. The idea of the Coffee project is, to count the consumed capsules

automatically by installing a web cam on top of the coffee machine. This web cam will

then detect the event of inserting a new capsule, recognize the capsules color and update

a counter for each capsule type. Figure 4.1 shows the experiment set-up.

The Coffee project was inspired by the well-known Trojan Room coffee pot camera

1 , which was installed at the University of Cambridge between 1991 and 1993. This

coffee maker, located in the so called Trojan Room at the Computer Laboratory of the

University of Cambridge, was ”the inspiration for the world’s first web cam”. It provided

an 128 ×128 pixel grayscale picture of the coffee maker within the local network of the

university. This enabled everybody working at the Computer Lab to check the status



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