FPGA-based real-time optical-flow system

Díaz, Javier; Ros, Eduardo; Pelayo, Francisco J.; Ortigosa, Eva M.; Mota, Sonia

doi:10.1109/tcsvt.2005.861947

Cited by 172 publications

(95 citation statements)

References 14 publications

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“…By learning proper parameters from training image sequences with ground truth, learning-based methods [56,61] partially handle this problem. (v) Real-time techniques: real time is crucial in practice; however, few of them can run in real time or in high frame rates, such as Lucas-Kanade method [7,45], parallel optical flow [10], and some hardware-based ones [28,53]. (vi) Energy-based methods [35] and phase-based techniques [32].…”

Section: Related Workmentioning

confidence: 99%

Real-time one-dimensional motion estimation and its application in computer vision

Yang

Gong

Tang

et al. 2015

Machine Vision and Applications

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Traditional optical flow methods are designed to generate two-dimensional (2D) motion fields. However in some applications, only the motion field along a predefined line is needed, while the rest of the redundant 2D region (Cong et al., CVPR, 2009) can be ignored. In this paper, we propose a general framework for estimating one-dimensional (1D) motion field using the L 1 norm metric, which can be flexible for adding any constraints depending on the specific intention. Compared with 2D methods in model calculation, which often obtain a local optimal solution, our 1D version has the advantage of real-time global optimization and sub-pixel accuracy with a large displacement capacity. The synthesized experiments have justified the effectiveness of our 1D model. Two vision applications are investigated: (i) for crowd counting, we design a novel unified framework for both line of interest and region of interest counting using our 1D motion model to mosaic the dynamic blobs. Such a framework can adapt to new site without scene-specific learning and overcome overfitting. (ii) For visual odometry, we apply our 1D model to estimate the linear velocity of the robot in real time and obtain a comparable result with the benchmark Encoder. Experiments verified that our proposed model can produce satisfactory results in real time.

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Section: Related Workmentioning

confidence: 99%

Real-time one-dimensional motion estimation and its application in computer vision

Yang

Gong

Tang

et al. 2015

Machine Vision and Applications

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“…Optical flow can also be used to detect independent moving objects in the presence of camera motion. Although many flow-computation methods are complex and currently inapplicable in realtime, different reliable FPGA-based Real-time Optical-flow approaches have been proposed in the recent years (Martin et al, 2005;Diaz et al, 2006). Furthermore, other processing image techniques like super resolution, atmospheric compensation and compressive sampling may be useful in enhancing the images and to reconstruct important aspect of the scenes.…”

Section: Fpga Applications In Image Processing and Computer Visionmentioning

confidence: 99%

Design of Embedded Augmented Reality Systems

Toledo¹,

Martínez²,

Garrigós³

et al. 2010

Augmented Reality

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“…The ever-increasing density of the CMOS technology market by the Moore's Law means that the designers can choose FPGAs as a proof of concept system (Lopich & Dudek, 2005) or even as an end system (Diaz et al, 2006). The time to market is another major reason why FPGAs are becoming increasingly competitive.…”

Section: Fpgamentioning

confidence: 99%