Massively parallel Lucas Kanade optical flow for real-time video processing applications

Plyer, Aurélien; Besnerais, Guy Le; Champagnat, Frédéric

doi:10.1007/s11554-014-0423-0

Cited by 95 publications

(54 citation statements)

References 35 publications

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“…While these solutions focus on accuracy rather than running time, accelerating the estimation of optical flow to real time (> 10f ps) processing also drew much research interest. In [10], the authors present eFOLKI, derived from the multi-scale iterative Lucas Kanade [8] algorithm. To speedup the computation, they reduce the number of pixel interpolations by wrapping the whole image after each iteration of the gradient descent.…”

Section: Related Workmentioning

confidence: 99%

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Fast Semi Dense Epipolar Flow Estimation

Garrigues

Manzanera

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Optical flow computation consists in recovering the apparent motion field between two images with overlapping fields of view. This paper focuses on a subset of optical flow problems, called epipolar flow, where the camera moves inside a scene containing no moving objects. Accurate solutions exist but their high computational complexities make them non suitable for a large panel of real-time applications.We propose a new epipolar flow approach with low computational complexity achieving the best error rate on the non dense KITTI optical flow 2012 benchmark and running 1000× faster than the second ranked approach. On a 4core 3GHz processor, our multi-core implementation computes a semi dense optical flow field of a 450k pixels image in 260ms. It is a significant advance in reducing the running time of accurate optical flow computation.To achieve such results we rely on the epipolar constraints and the local coherence of the optical flow not only to increase accuracy but also to reduce computational complexity.Our contribution is twofold. It is first, the acceleration and the accuracy increase of current RANSAC based visual odometry algorithms via the estimation of a robust sparse flow field, well distributed over the image domain. And then, the estimation of a semi dense flow field leveraging epipolar constraints and a propagation scheme to speedup the estimation and reduce error rates.

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Section: Related Workmentioning

confidence: 99%

“…• Its parallel implementation takes advantage of a multi-core CPU architecture as in [10] which compute the optical flow on a GPU.…”

Section: Related Workmentioning

confidence: 99%

Fast Semi Dense Epipolar Flow Estimation

Garrigues

Manzanera

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…• Additionally, Plyer et al [32] deal with dense optical flow estimation from the perspective of the tradeoff between the quality of the estimated flow and computational cost which is required by real-world applications. They propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on a GPU.…”

Section: In the Context Of Real-time Implementation Of Motion Estimatmentioning

confidence: 99%

Real-time motion estimation for image and video processing applications

Botella

Garcı́a

2014

J Real-Time Image Proc

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This contribution focuses on different topics that are covered by the special issue titled ''Real-Time Motion Estimation for image and video processing applications'' and which incorporate GPUS, FPGAs, VLSI systems, DSPs, and Multicores, among other platforms. The guest editors have solicited original contributions, which address a wide range of theoretical and practical issues related to high-performance motion estimation image processing including, but not limited to: real-time matching motion estimation systems, real-time energy-based motion estimation systems, gradient-based motion estimation systems, optical flow estimation systems, color motion estimation systems, multi-scale motion estimation systems, optical flow and motion estimation systems, analysis or comparison of specialized architectures for motion estimation systems and realworld applications.

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“…We present a moving object detection system based on eFOLKI, a newly proposed fast OF method [11] which allows real-time processing of large images. We present a complete analytical formulation of the uncertainty model of both direct and image prediction methods.…”

Section: Contribution and Outline Of The Papermentioning

confidence: 99%

“…Here we propose to use eFOLKI [11], a very fast OF algorithm based on Lucas-Kanade (LK) approach. Compared on the same GPU hardware, the runtime of eFOLKI is between one or two order of magnitude lower than variational methods such as TV-L1 [18] and Brox et al [19].…”

Section: B Low-level Operationsmentioning

confidence: 99%

Real-time mobile object detection using stereo

Derome

Plyer

Sanfourche

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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This paper considers passive vision for robotics and focuses on devising a real-time process for moving object detection using a stereo rig. As several previous works, our method relies on the use of dense stereo and of optical flow. Observing that the main computational load of existing methods is related to the estimation of the optical flow, we propose to use a fast algorithm based on Lucas-Kanade's paradigm. We derive a new uncertainty model which explicitly takes into account all errors originating from each estimation step of the process. In contrast with most previous works, we describe a rigorous expansion of the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the KITTI dataset, which demonstrates the effectiveness of the proposed approach.

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Massively parallel Lucas Kanade optical flow for real-time video processing applications

Cited by 95 publications

References 35 publications

Fast Semi Dense Epipolar Flow Estimation

Fast Semi Dense Epipolar Flow Estimation

Real-time motion estimation for image and video processing applications

Real-time mobile object detection using stereo

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