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 7.8.: The number of frames for each shot within the ”Tagesschau” news dataset,

distinguishing between anchorshots and ’normal’ shots.

Computational time optimization

Of course, it is not necessary to perform the anchorshot detection for each single frame.

In fact it is sufficient to test one frame per shot only. This enables us to significantly speed

the anchorshot detection process up. Furthermore, when looking at the data included in

the news dataset, it turns out that anchorshots are significantly longer than news report

shots. Table 7.9 shows this aspect by comparing the average number of frames of an

anchorshot and the average number of frames of a news report shot.

Date # of average # of # of other average # of

anchorshots frames shots frames

11.09.2012 11 412 129 138

13.09.2012 12 520 121 144

15.09.2012 12 383 96 144

27.10.2012 10 508 105 146

Total 48 452 450 143

Table 7.9.: Number of frames in anchorshots vs. other shots in ”Tagesschau” news shows.

In fact all anchorshots in the news dataset cover more than 100 frames. Hence it is very

unlikely, that short shots, covering less than four seconds (≈ 100 frames), are anchorshots.

Figure 7.8) illustrates this fact. Utilizing this fact as well, the computational time of the

anchorshot detection could be improved by the factor 100.


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