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

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

3. Machine Learning


Figure 3.8.: Example for an input stream, consisting of decoded MJepg Images read over

the Internet.

Conceptually, a data stream is defined as ”an interface, which essentially provides a

method to obtain the next item of a stream” [Bockermann and Blom, 2012b]. Each of

the data items consist of a set of (key, value)-pairs, where keys are attribute names and

values are the corresponding values. Figure 3.9 shows an example for one data item,

containing a serialized image plus some additional information.

Key

Value

frame ”stream.image.ImageRGB@2583d260”

frame:id 42

frame:width 1920

frame:height 1080

Figure 3.9.: A data item containing one image plus some additional information.

”The names are required to be of type String whereas the values can be of any type

that implements Java’s Serializable interface. The data item is provided by the

stream.Data interface.”

Processors

Processors are ”the low-level functional units that actually do the data processing and

transform the data items”. They basically have one method that receives a data item,

manipulates it, and returns the modified (or even a totally new) data item as its result.

Furthermore it offers the possibility to perform some initializations when the process

gets started. The basic framework for a processor can be found in the appendix.

Processes

The streams framework ”defines a process as the consumer of such a source of items

[=stream]. A process is connected to a stream and will apply a series of processors to

each item that it reads from its attached data stream.”

Stream processes are defined in a XML. In such a XML the stream source as well as the

processors are configurated. Figure 3.10 shows an example for a configuration XML.

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