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

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

7.2. Segmentation and Tagging of News shows

furnishings or the background. The advantage of this model is that it can be updated

anytime a new anchorshot is found by simply comparing the new anchorshot to the old

model and exclude pixels that differ. Figure 7.7 shows an example for the resulting mask.

Excluded pixels are represented as black pixels, whereas all colored pixels are pixels that

had this color (± a limit of tolerance) throughout all anchorshot frames seen so far. The

presented mask has been inferred automatically by using the AnchorshotModelCreator

processor, I have implemented for the streams framework.

Figure 7.7.: Automatically inferred model for anchorshots using the labeled data included

in the news dataset.

First anchorshot frame of the show

We know that each ”Tagesschau” show starts with an intro, directly followed by an

anchorshot. As the intro of each ”Tagesschau” show is always the same and covers

approximately 250 frames, we can assume, that the 300th frame of a ”Tagesschau” news

show is an anchorshot. Hence we can compare each following frame to the 300th frame

and compute the pixel-wise color difference. If at least 50% of the pixels are the same (±

a limit of tolerance), I classify the frame as an anchorshot frame. Surprisingly this easy

approach turns out to perform very well, resulting in an accuracy of 100%. As table 7.8

shows, there were no misclassifications at all when applying this on the ”Tagesschau”

news video from September 11th, 2012.

Prediction: true Prediction: false Total

Label: True 5.783 0 5.783

Label: False 0 17.678 17.678

Total 5.783 17.678 23.461

Table 7.8.: Resulting confusion matrix for the anchorshot detection experiment using

the pixel-wise comparision of each frame with the 300th frame for the show.

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