Determining optical flow using a differential method

Bainbridge-Smith, Andrew; Lane, R.

doi:10.1016/s0262-8856(96)01113-4

Cited by 39 publications

(20 citation statements)

References 14 publications

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“…To measure motion, for each pixel belonging to an object, we compute the spatiotemporal differential equations for optical flow approximation, in accordance with [18]. The average optical flow computed over all the pixels of an MVO blob is the figure we use to discriminate between MVOs and ghosts: In fact, MVOs should have significant motion, while ghosts should have a near-to-zero average optical flow since their motion is only apparent.…”

Section: Detecting Moving Objects Ghosts and Shadowsmentioning

confidence: 99%

Detecting moving objects, ghosts, and shadows in video streams

Cucchiara

Grana

Piccardi

et al. 2003

IEEE Trans. Pattern Anal. Machine Intell.

1,240

727

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Abstract-Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. This work proposes a general-purpose method that combines statistical assumptions with the objectlevel knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.

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Section: Detecting Moving Objects Ghosts and Shadowsmentioning

confidence: 99%

Detecting moving objects, ghosts, and shadows in video streams

Cucchiara

Grana

Piccardi

et al. 2003

IEEE Trans. Pattern Anal. Machine Intell.

1,240

727

View full text Add to dashboard Cite

show abstract

“…Therefore, although (3) is derived from the gradient constraint equation, it does not necessarily impose it. For this purpose, we explicitly include the gradient constraint equation in our linear system to obtain (4) This yields a pixelwise formulation that includes both first-order and second-order constraints. In practice, due to various sources of error, (4) does not identically vanish.…”

Section: Proposed Approachmentioning

confidence: 99%

Pixelwise-adaptive blind optical flow assuming nonstationary statistics

Foroosh

2005

IEEE Trans. on Image Process.

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Abstract-In this paper, we address some of the major issues in optical flow within a new framework assuming nonstationary statistics for the motion field and for the errors. Problems addressed include the preservation of discontinuities, model/data errors, outliers, confidence measures, and performance evaluation. In solving these problems, we assume that the statistics of the motion field and the errors are not only spatially varying, but also unknown. We, thus, derive a blind adaptive technique based on generalized cross validation for estimating an independent regularization parameter for each pixel. Our formulation is pixelwise and combines existing first-and second-order constraints with a new second-order temporal constraint. We derive a new confidence measure for an adaptive rejection of erroneous and outlying motion vectors, and compare our results to other techniques in the literature. A new performance measure is also derived for estimating the signal-to-noise ratio for real sequences when the ground truth is unknown.

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“…Considering all these factors and in accordance with the analysis of different algorithms and their benefits [24,25], the Lucas-Kanade method, which has good quality results with moderate computational cost, has been chosen. Specifically, the sequential implementation by Correia at the Biomedical Engineering Institute, Engineering School of the University of Porto [26,27] has been used.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of the optical flow computation in sequences from moving cameras

et al. 2014

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This paper presents a flexible and scalable approach to the parallelization of the computation of optical flow. This approach is based on data parallel distribution. Images are divided into several subimages processed by a software pipeline while respecting dependencies between computation stages. The parallelization has been implemented in three different infrastructures: shared, distributed memory, and hybrid to show its conceptual flexibility and scalability. A significant improvement in performance was obtained in all three cases. These versions have been used to compute the optical flow of video sequences taken in adverse conditions, with a moving camera and natural-light conditions, on board a conventional vehicle traveling on public roads. The parallelization adopted has been developed from the analysis of dependencies presented by the well-known Lucas-Kanade algorithm, using a sequential version developed at the University of Porto as the starting point.

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Determining optical flow using a differential method

Cited by 39 publications

References 14 publications

Detecting moving objects, ghosts, and shadows in video streams

Detecting moving objects, ghosts, and shadows in video streams

Pixelwise-adaptive blind optical flow assuming nonstationary statistics

Parallelization of the optical flow computation in sequences from moving cameras

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