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Journal Articles Journal of Real-Time Image Processing Year : 2023

Optical flow algorithms optimized for speed, energy and accuracy on embedded GPUs

Abstract

Embedded Computer Vision is a hot field of research that requires trade-offs in order to balance execution time, power consumption and accuracy. In that field, dense optical flow estimation is a major tool used in many applications. Many algorithms have been designed, focusing on accuracy, very few works address trade-offs and implementation on embedded hardware. This paper tackles these trade-offs for embedded GPU through the example of the well-known TV-L 1 algorithm. Thanks to High Level Transforms-operator fusion and pipeline-and taking into account the iterative aspect of these algorithms, we achieve a speedup of ×3.7 versus OpenCV. Moreover, we show that a 16bit half precision implementation has a higher accuracy than the 32-bit precision one for the same frame processing time on NVIDIA Jetson boards. Furthermore, this work can be generalized to any kind of iterative stencil-based algorithms.
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Dates and versions

hal-04199828 , version 1 (08-09-2023)
hal-04199828 , version 2 (11-09-2023)

Identifiers

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Thomas Romera, Andrea Petreto, Florian Lemaitre, Manuel Bouyer, Quentin L. Meunier, et al.. Optical flow algorithms optimized for speed, energy and accuracy on embedded GPUs. Journal of Real-Time Image Processing, 2023, 20 (2), pp.32. ⟨10.1007/s11554-023-01288-6⟩. ⟨hal-04199828v2⟩
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