Abstract
A constraint of real-time implementation of differential motion detection algorithms is the large amount of data to be processed. Full image processing is usually the classical approach for these algorithms: spatial and temporal derivatives are calculated for all pixels in the image despite the fact that the majority of image pixels may not have changed from one frame to the next. By contrast, the data flow model works in a totally different way as instructions are only fired when the data needed for these instructions are available. Here we present a method to speed-up low level motion detection algorithms. This method is based on pixel change instead of full image processing and good speed-up is achieved.
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Boluda, J.A., Pardo, F. (2007). Speeding-Up Differential Motion Detection Algorithms Using a Change-Driven Data Flow Processing Strategy. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_10
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DOI: https://doi.org/10.1007/978-3-540-74272-2_10
Publisher Name: Springer, Berlin, Heidelberg
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