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a System configuration of a video surveillance processing chain.
b Feature model full configuration of a video surveillance processing chain.
Figure 2(a) shows a configuration instance of the video surveillance processing chain.
Figure 1 depicts an example of feature model for a video surveillance processing chain, as described in (Moisan et al. 2011).
In our video surveillance processing chain, we rename this performance measure as frame processing time, i.e., the time required to process a video frame.
Our approach can be viewed as an instance of the MAPE-K loop, as follows: the managed element is a video surveillance processing chain implemented in terms of software components; a feature model formalism is used for the knowledge representation (about the system and its context); and an optimization process based on an heuristic search algorithm is used for the planning step.
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In (Moisan et al. 2011), feature models were proposed for the representation and dynamic adaptation of component-based systems, such as a video surveillance (VS) processing chain.
In the domain of computer vision and video surveillance systems, the processing chain can involve additional stages (e.g., clustering, shadow removal, and data fusion - in case of multiple cameras), which require the deployment of different software components.
When processing real video surveillance sequences, BS algorithms face with several challenges such as sudden illumination changes, movement turbulence, etc. [2].
As to reduce processing load for video surveillance embedded systems, three low-level motion detection algorithms to be implemented on an analog CMOS image sensor are presented.
Among the 10 papers dealing with early processing stages in video surveillance, two are in the area of tracking, five devoted to background subtraction, and three relate to object detection.
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