Optical Flow Computation on Compute Unified Device Architecture

Mizukami,; Tadamura,

doi:10.1109/iciap.2007.4362776

Cited by 30 publications

(19 citation statements)

References 11 publications

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“…Just to name a few of those studies, Mizukami and Tadamura [20] proposed implementation of Horn and Schunck's regularization algorithm with a multiscale search method for optical flow computation. They were able to achieve speedup of approximately 16 on a NVIDIA GeForce 8800 GTX card over a 3.2-GHz CPU.…”

Section: Related Workmentioning

confidence: 99%

“…The general optimization strategies include utilizing many threads and maximizing memory and instruction throughput through a set of techniques such as global memory coalescing, reducing shared memory bank conflicts, and reducing divergent branching [22]. Both of [20] and [5] applied these strategies to achieve optimal performance. However, they simply take the processing of each pixel as a computation unit for parallelization, none of them explored the influence of level of parallelism on the optimization.…”

Section: Related Workmentioning

confidence: 99%

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Performance analysis of a novel GPU computation-to-core mapping scheme for robust facet image modeling

Park

Cao

Watson

et al. 2012

J Real-Time Image Proc

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Modern graphics processing units (GPUs) are commodity data-parallel coprocessors capable of high performance computation and data throughput. It is well known that the GPUs are ideal implementation platforms for image processing applications. However, the level of efforts and expertise to optimize the application performance is still substantial. This paper investigates the computation-to-core mapping strategies to probe the efficiency and scalability of the robust facet image modeling algorithm on GPUs. Our fine-grained computation-to-core mapping scheme achieves a significant performance gain over the standard pixel-wise mapping scheme. With indepth performance comparisons across the two different mapping schemes, we analyze the impact of the level of parallelism on the GPU computation and suggest two principles for optimizing future image processing applications on the GPU platform.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Performance analysis of a novel GPU computation-to-core mapping scheme for robust facet image modeling

Park

Cao

Watson

et al. 2012

J Real-Time Image Proc

View full text Add to dashboard Cite

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“…The software was programmed using the CUDA library (Compute Unified Device Architecture) to compute dense and accurate velocity field at about 15 fps with 640x480 video resolution. Authors in [7] presented the CUDA implementation of the Horn-Shunck optical flow, that offered a real-time processing of 316×252 video resolution. Gwosdek et al [8] developed a GPU implementation of the Euler-Lagrange (EL) framework for solving variational optical flow methods using sequences with 640x480 pixels in near-real-time.…”

Section: Related Workmentioning

confidence: 99%

Multi-GPU based framework for real-time motion analysis and tracking in multi-user scenarios

Mahmoudi¹

2015

EAI Endorsed Transactions on Creative Technologies

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Video processing algorithms present a necessary tool for various domains related to computer vision such as motion tracking, event detection and localization in multi-user scenarios (crowd videos, mobile camera, scenes with noise, etc.). However, the new video standards, especially those in high definitions require more computation since their treatment is applied on large video frames. As result, the current implementations, even running on modern hardware, cannot provide a real-time processing (25 frames per second, fps). Several solutions have been proposed to overcome this constraint, by exploiting graphic processing units (GPUs). Although they exploit GPU platforms, they are not able to provide a real-time processing of high definition video sequences. In this work, we propose a new framework that enables an efficient exploitation of single and multiple GPUs, in order to achieve real-time processing of Full HD or even 4K video standards. Moreover, the framework includes several GPU based primitive functions related to motion analysis and tracking methods, such as silhouette extraction, contours extraction, corners detection and tracking using optical flow estimation. Based on this framework, we developed several real-time and GPU based video processing applications such as motion detection using moving camera, event detection and event localization

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“…The software was programmed using the CUDA library (Compute Unified Device Architecture) to compute dense and accurate velocity field at about 15 frames per second (FPS) for the image resolution of 640×480. Authors in [23] presented the CU-DA implementation of the Lucas-Kanade optical flow method with a real-time processing (25 FPS) of low resolution videos (316×252). This method produces dense displacement field based on a straightforward processing procedure.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, the primary application of GPUs is still the image and video processing [18][19][20][21]. Yet, even though several approaches to the problem of motion estimation have been published lately, including those taking advantage of modern GPUs [22][23][24][25], they are either unable to handle high definition video streams or are limited to a single GPU and thus do not scale up well. Therefore, in response to presented needs we decided to implement a highly parallel multi-GPU version of the Lucas-Kanade algorithm for motion estimation.…”

Section: Introductionmentioning

confidence: 99%

Real-time motion tracking using optical flow on multiple GPUs

Mahmoudi¹,

Kierzynka²,

Manneback³

et al. 2014

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. Motion tracking algorithms are widely used in computer vision related research. However, the new video standards, especially those in high resolutions, cause that current implementations, even running on modern hardware, no longer meet the needs of real-time processing. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have recently been proposed. Although they present a great potential of a GPU platform, hardly any is able to process high definition video sequences efficiently. Thus, a need arose to develop a tool being able to address the outlined problem.In this paper we present software that implements optical flow motion tracking using the Lucas-Kanade algorithm. It is also integrated with the Harris corner detector and therefore the algorithm may perform sparse tracking, i.e. tracking of the meaningful pixels only. This allows to substantially lower the computational burden of the method. Moreover, both parts of the algorithm, i.e. corner selection and tracking, are implemented on GPU and, as a result, the software is immensely fast, allowing for real-time motion tracking on videos in Full HD or even 4K format. In order to deliver the highest performance, it also supports multiple GPU systems, where it scales up very well.

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Optical Flow Computation on Compute Unified Device Architecture

Cited by 30 publications

References 11 publications

Performance analysis of a novel GPU computation-to-core mapping scheme for robust facet image modeling

Performance analysis of a novel GPU computation-to-core mapping scheme for robust facet image modeling

Multi-GPU based framework for real-time motion analysis and tracking in multi-user scenarios

Real-time motion tracking using optical flow on multiple GPUs

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