Processor Architecture: From Dataflow to Superscalar and Beyond
Processor Architecture: From Dataflow to Superscalar and Beyond
A new motion detection algorithm based on Σ-Δ background estimation
Pattern Recognition Letters
On the Advantages of Asynchronous Pixel Reading and Processing for High-Speed Motion Estimation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Selective Change-Driven Image Processing: A Speeding-Up Strategy
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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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.