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

ai.cs.uni.dortmund.de

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

2.5. Further related work

a-priori knowledge, other approaches base on databases that contain known commercial

spots and compare the video stream to the stored spots. The database

can then be expanded by adding segments between known commercial spots and

assuming, they are advertisement as well. A-priori knowledge includes that advertisement

spots are full of motion, ending with a rather still shot, presenting the

product. Furthermore the audio signal is mostly tuned up during commercials and

in many cases two commercial spots are separated by some monochrome frames.

Lienhart, Kuhmünch, and Effelsberg [Lienhart et al., 1997a] for example have applied

both approaches on German television data, gaining very good results.

News story names Anchorshots usually contain a textual label, which briefly summarizes

what the following news story is about. This captions can be read out using

Optical Character Recognition (OCR) software [Sato et al., 1999]. Approaches

include locating text areas, applying filters to sharpen the text and keyword detection.

Events Beside its application in the tagging of news videos, OCR can be of benefit

in other settings as well. By reading out the overlaid scores in sport broadcasts,

Babaguchi et al. [Babaguchi et al., 2004] were for example able to detect highlights

in football games. They used their method to automatically create short video clips,

containing all highlights of a game.

Actors By using face detection methods, some approaches try to identify certain actors

in video data. As actors usually act in costumes and with makeup on, this turns

out to be very complex by just matching the detected faces against a database

of actors’ faces. Some approaches recognize the reoccurrence of certain characters

within the same movie [Lienhart et al., 1997b]. By taking into account EPG-data

as well, this can help to identify the main characters and provides useful tags for

video data as well.

2.5. Further related work

In many applications, video data comes along with audio data. Hence it suggests itself

to take into account audio data to improve the quality of the segmentation of the video

data. The following section gives an overview of the existing approaches that handle

audio data.

2.5.1. Audio analyze

Daxenberger [Daxenberger, 2007] gives a good overview of the state of the art in the

field of audio segmentation. The described approaches do no take into account any video

data and have mainly been designed to segment audio data like music files. Daxenberger

has furthermore developed an application called Segfried, which is able to automatically

segment audio data using different segmentation techniques. The implemented segmentation

algorithms include a segmentation by Support Vector Machines (SVMs), and a

metric pair-wise constraints k-Means Clustering (MPCK-Means).

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