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

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

4.2. Use case B: Coffee - Project

of the coffee maker from their computer before actually walking to the Trojan Room.

Hence useless trips to the coffee maker could be avoided.

Figure 4.1.: The experiment experiment set-up for the coffee capsule project. A web cam,

installed on the coffee maker, continuously films the closing lever of the machine. If a

capsule is inserted, the event can be detected and the capsule can be classified by its

color. The web cam is connected to a computer, processing the incoming video data

and making it available over TCP/IP.

The approach to count coffee capsules automatically is -like Trojan Room coffee pot

camera- a helpful and demonstrative application to apply techniques, which are useful

for other tasks as well. To be able to count the coffee capsules automatically, I have to

recognize the capsules that are inserted in the machine. Hence the learning tasks of this

project are

• B1: Identify all events, where a new coffee capsule is inserted in the coffee maker

by supervising the incoming video stream. As events usually last for more then one

frame, it is of course not necessary to detect all frames covered by the event. An

event is rather said to be detected, as soon as at least one of the frames covered

by the event is detected. Again precision and recall are chosen as appropriate

evaluation criteria.

• B2: Recognize the type of the capsule by identifying its’ color. In this setting I

am again interested in the percentage of correctly classified capsules based on all

capsules. Hence the accuracy is chosen as the suitable evaluation criterion.


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