A comparison of correntropy-based feature tracking on FPGAs and GPUs

Embedded signal-processing applications often require feature tracking to identify and track the motion of different objects (features) across a sequence of images. Common measures of similarity for real-time usage are either based on correlation, mean-squared error, or sum of absolute differences,...

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Bibliographic Details
Main Authors: Cooke, Patrick, Fowers, Jeremy, Stitt, Greg, Hunt, Lee
Format: Conference Proceeding
Language:English
Subjects:
Circuit faults
correntropy
Feature extraction
feature tracking
Field programmable gate arrays
FPGA
GPU
Graphics processing units
optical flow
Random access memory
Real-time systems
Robustness
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Summary:Embedded signal-processing applications often require feature tracking to identify and track the motion of different objects (features) across a sequence of images. Common measures of similarity for real-time usage are either based on correlation, mean-squared error, or sum of absolute differences, which are not robust enough for safety-critical applications. A recent feature-tracking algorithm called C-Flow uses correntropy to significantly improve signal-to-noise ratio. In this paper, we present an FPGA accelerator for C-Flow that is typically 2-7x faster than a GPU and show that the FPGA is the only device capable of real-time usage for large features. Furthermore, we show the FPGA accelerator is generally more appropriate for embedded usage, with energy consumption that is often 1.2-7.9x less than the GPU.
ISSN:1063-6862
DOI:10.1109/ASAP.2013.6567580