Abstract
Optical flow computation is a well-known topic with a large number of contributions describing different models and their accuracies but real-time implementation of high frame-rate sequences remains as an open issue. The presented approach implements a novel superpipelined and fully parallelized architecture for optical flow processing with more than 70 pipelined stages that achieve a data throughput of one pixel per clock cycle. This customized DSP architecture is capable of processing up to 45 Mpixels/s arranged for example as 148 frames per second at VGA resolution (640x480 pixels). This is of extreme interest in order to use high frame-rate cameras for reliable motion processing. We justify the optical flow model chosen for the implementation, analyze the presented architecture and measure the system resource requirements. Finally, we evaluate the system comparing its performance with other previous approaches. To the best of our knowledge, the obtained performance is more than one range of magnitude higher than any previous real-time approach described in the literature.
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Díaz, J., Ros, E., Mota, S., Rodriguez-Gomez, R. (2006). Highly Paralellized Architecture for Image Motion Estimation. In: Bertels, K., Cardoso, J.M.P., Vassiliadis, S. (eds) Reconfigurable Computing: Architectures and Applications. ARC 2006. Lecture Notes in Computer Science, vol 3985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802839_11
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DOI: https://doi.org/10.1007/11802839_11
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