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

Pred: Pred: Pred: Pred: Pred: Pred: class

red yellow blue green black purple recall

Label: red 12 0 0 0 0 0 100 %

Label: yellow 0 12 0 0 0 0 100 %

Label: blue 0 0 12 0 0 0 100 %

Label: green 0 0 0 11 0 1 91.7 %

Label: black 0 0 0 0 10 2 83.3 %

Label: purple 0 0 0 0 5 7 58.3 %

class precision 100 % 100 % 100% 100 % 66.7 % 70.0 %

Table 7.10.: Confusion matrix for the capsule color detection using LibSVM.

Beside the LibSVM, I have tested other learning algorithms. The classification results

can be found in table 7.11.

Obviously the detection of certain capsules is quite easy. Red, yellow and blue capsules

get recognized easily, the predictions for green are sufficient as well. Only the black and

the purple capsules can hardly be distinguished. I believe, that the reason for this is the

poor quality and the bad light circumstances of the video data. When pointing a spot

light on the capsules, at the time they are filmed, the classification will surely become

better. Nevertheless an accuracy of more than 85% is quite good in a setting, with six

equally distributed classes. Random guessing would after all result in less than 20%

accuracy.

algorithm accuracy optimal parameters

k-NN 79.46 % ± 15.18 % k=1

Naive Bayes 76.86 % ± 11.52 %

Neural Net 76.43 % ± 10.97 % training cycles = 63, learning

rate = 0.863, momentum

= 0.564

Decision Tree 63.39 % ± 16.39 % criterion = gain ratio, minimal

gain = 0.05, minimal

leaf size = 1, minimal size

for split = 3

Table 7.11.: Results for the coffee capsule recognition using different machine learning

algorithms.

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