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

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

7. Experiments and Evaluation

Figure 7.6.: Stream Framework process to detect anchorshots by applying the decision

tree inferred with RapidMiner.

The decision tree was inferred on the labeled ”Tagesschau” news videos from September

13th and 15th, and October 27th, 2012. Hence it makes sense to apply it on the ”Tagesschau”

news video broadcasted on September 11th, 2012. Based on this news video, the

precision of the detection approach reaches 97% (recall = 85.8%). Unfortunately only

half of the real anchorshots really get labeled as anchorshots. This is of course not sufficient,

but can be improved by using a decision tree, which is deeper and hence takes

into account more features.

Prediction: true Prediction: false Total

Label: True 2.966 2.817 5.783

Label: False 529 17.149 17.678

Total 3.495 19.966 23.461

Table 7.7.: Resulting confusion matrix for the anchorshot detection experiment shown

in figure 7.6

Applying an Image-Model

But not only decision trees are reasonable models for representing anchorshots. Anchorshots

can also be represented by images. I have considered two types of images.

Inferred anchorshot mask

Based on labeled data, we can evaluate, which pixels in anchorshot frames never change

at all. This includes all segments of anchorshot frames, which belong to the studio


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