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

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

7. Experiments and Evaluation

channels decrease significantly. In this sequence they all drop clearly below 90. Hence

my first approach for detecting events in the ”coffee”-dataset bases on the extraction of

the average color values for all RGB color channels. Based on this, a simple classifier

tests, whether the value for one of the color channels dips more than a fixed threshold t,

or not. In case it does, the frame is classified as ”belongs to an event”. The corresponding

configuration file for running this with the streams framework is shown in figure 7.10.














Figure 7.10.: Streams Framework process to detect events in one of the coffee capsule

video file by applying the simple classifier using a threshold.

As already mentioned in chapter 5.2.2, an event ranges over more than one frame. Nevertheless

it does not matter, if the event detection algorithms does not label all of the

frames, belonging to the event, as such. In fact it is sufficient, when at least one of the

frames is detected. Hence I needed a new evaluation processor to determine the quality

of the classifier. This new processor is named stream.coffee.eventdetection.

EventDetectionEvaluation processor and further described in the appendix.

Running the experiment, it turns out that this easy classifier is sufficient to solve the

learning task. On both video data files included in the dataset, it detects all events,

without ever proclaiming an event, where no event is. Hence, precision as well as recall

for this classifier are 100% on the given data. Of course the ”standardvalue” and the

”threshold” used by the classifier will have to be adjusted to the data, produced by a

web cam, which is installed on the coffee maker directly.

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