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

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

5. Data

5.2.2. The ”coffee”-dataset

For the evaluation of the learning tasks B1 and B2, I use a dataset consisting of two

short video sequences. Each of the video files shows 36 coffee capsules, which slip down a

slide. A capsule slipping down the slide is hence one ”event”, whilst the empty slide can

be seen as ”no event”. The video files are recorded at a stretch, using a webcam with

a resolution of 640 × 480 pixels per frame. 25 frames per second are captured. The 36

coffee capsules split into six different classes, representing six different coffee flavors. All

flavors can be identified by the color of the capsules. Possible colors are ”red”, ”yellow”,

”blue”, ”green”, ”black”, and ”purple”.

I have decided to produce the labeled dataset by slipping coffee capsules down a slide

for two reasons: First of all, the Nespresso coffee maker in our office broaches all coffee

capsules that are inserted. Hence it would not have been possible to create an artificial

dataset within a short amount of time, without having to drink a whole lot of coffee.

Furthermore I was not sure, if I would be able to manually determine the color of coffee

capsules, which where inserted over time, in order to label the data correctly. Especially

purple, dark green, and black capsules did almost look the same to me. So I decided to

create the dataset artificially. The inferred techniques for event detection and capsule

recognition will afterwards be transferable to the real setting easily.

Beside the video data, the dataset contains files, which hold the true labels for both

of the video files. This includes the number of the frame, where an event occurs, plus

the color of the coffee capsule, which is slipping down the slide at that moment. As an

event spans more than one frame, only the first frame of the event is stored in the label

file. Implicitly the following six frames belong to the same event. Figure 5.11 shows an

example for one capsule event.

An example for a file, containing the manually assigned labels for one of the two video sequences,

is included in the appendix. The full dataset, including the video data as well as

the label files, is available online. The url is http://mattis.special-operations.


Figure 5.11.: A red coffee capsule slipping down the slide. All six consecutive frames

belong to the same capsule event.


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