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

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

5.2. Creation of datasets

night from 8:00pm to 8:15pm. The reason for downloading the videos from YouTube is,

that they have the identical video format as the onces we receive when using the ZAPI.

The advantage is, that I did not have to glue together the MPEG Transport Stream

(.ts) segments, which made the process of creating the dataset a little bit easier. The

downloaded videos are listed in tabular 5.1.

Date

Length in minutes

Tuesday, September 11th 2012 15:38

Thursday, September 13th 2012 15:44

Saturday, September 15th 2012 12:21

Saturday, October 27th 2012 13:36

Total 57:19

Table 5.1.: Video data included in the ”news” dataset

Each of the video files has a resolution of 1280 × 720 pixels per frame (HD ready). In

order to be able to process the data in RapidMiner,the video files were first encoded

using xuggler. As the result I get a folder containing images of each single frame (see

figure 5.10. Based on the encoded data, I attached labels to the resulting JPEG images.

The first multinominal label is ”cut” (C), ”gradual transition” (GT), and ”not cut”

respectively. The second label holds information about the type of the shot. It distinguishes

between ”anchorshots” (AS) and ”no anchorshots”. The ”no anchorshot” class

is further labeled as ”intro”, ”extro”, ”weather” and ”n a”, which covers all sort of news

report shots including interviews and commentaries. By using mencoder,the video files

have furthermore been converted into a format that can be imported in the streams

framework. The video files as well as the .csv files containing the labels are available

online. The url is http://mattis.special-operations.de/video/. The labels

of one news show are furthermore included in the appendix.

Figure 5.10.: Images resulting from decoding a video with xuggler. Each frame gets stored

as a JPEG image.

57

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