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

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

7.3. Coffee capsule recognition

7.3. Coffee capsule recognition

The second use case is about detecting and recognizing coffee capsules in video data.

It splits in two task: the event detection and capsule recognition. Both problems are

tackled in this chapter.

7.3.1. Event Detection

Figure 5.11 has already given us an idea, how the frames that belong to one event

look like. Obviously the images of all frames, which do not belong to an event, do not

differ too much. But whenever a coffee capsule slips by, the color of the capsule changes

the image extremely. Hence I started my experiments by calculating the average color

values of all three RGB color channels for each image and plotted the data, using the

stream.plotter.Plotter processor, which comes with the streams framework. The

resulting plot is shown in figure 7.9.

Figure 7.9.: Average RGB values for capsule events.

The plot visualizes the average RGB color values for a sequence of 500 frames, taken

from one of the video files included in the ”coffee”-dataset. The sequence covers 9 events

of coffee capsules slipping down the slide. Obviously, the assumption that the average

RGB color values are a good indicator for recognizing events, is supported: as long as

no capsule slips by, the average RGB color values of the frames do not change a lot.

The average blue value (in this plot visualized by the red line) is somewhere around

110 ± 10, the average red and green color values are somewhere around 120 ± 10. As

soon as the frame shows a coffee capsule, the average color values for all three RGB

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