High Accuracy Optical Flow Estimation Based on a Theory for Warping

Brox, Thomas; Bruhn, Andr ́ es; Papenberg, Nils; Weickert, Joachim

doi:10.1007/978-3-540-24673-2_3

Cited by 2,065 publications

(2,056 citation statements)

References 23 publications

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“…The fusion process consists of a logical OR of the two approaches' foreground masks and then the use of heuristic reasoning to permit the motion detector's regions to split change regions that have inhomogeneous motion, while the regions of the change detector are permitted to merge motion regions that show homogeneous motion. For this work, change detection is performed using the Adaptive Gaussian Mixture Model of [1], while real-time optical flow is provided by OpenCV's GPU implementation of [19].…”

Section: Object Detectionmentioning

confidence: 99%

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Burghouts¹,

Schutte²,

Hove³

et al. 2014

SIViP

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Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck's cargo occurs. The theft takes place in very different forms, in the midst of many people who pose no threat. The threats range from explicit, e.g., a person attacking the truck driver, to implicit, e.g., somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person's activity, presence in a particular zone, and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The semantic representation encodes the notions of trajectories, zones and activities. The aim of this representation is to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person's activities are the most important elements of this semantic representation, followed by the person's trajectory. The proposed threat detection system is very accurate: 96.6% of the tracks are correctly interpreted, when considering the temporal context.

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Section: Object Detectionmentioning

confidence: 99%

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Burghouts¹,

Schutte²,

Hove³

et al. 2014

SIViP

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“…The best results in terms of accuracy were obtained by Brox et al [13]. The global energy is only linearized inside the minimization algorithm after warping the image at time t+1 on to the image at time t. [14] further extends optical flow into scene flow estimation.…”

Section: Related Workmentioning

confidence: 99%

“…They are computed from image warping techniques similar to the method described in [13]. Three color channels R, G and B are used in the data term and c denotes one of 3 color channels.…”

Section: Scene Flow Estimationmentioning

confidence: 99%

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Disparity Estimation in Stereo Sequences using Scene Flow

Liu

Philomin

2009

Procedings of the British Machine Vision Conference 2009

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This paper presents a method for estimating disparity images from a stereo image sequence. While many existing stereo algorithms work well on a single pair of stereo images, it is not sufficient to simply apply them to temporal frames independently without considering the temporal consistency between adjacent frames. Our method integrates the state-of-the-art stereo algorithm with the scene flow concept in order to capture the temporal correspondences. It computes the dense disparity images and scene flow in a practical and unified process: the disparity is initialized by a hybrid stereo approach which employs the over-segmentation based stereo and pixelwise iterative stereo; then the scene flow, estimated via a variational approach, is used to predict the disparity image and to compute its confidence map for the next frame. The prediction is modeled as a prior probability distribution and is built into an energy function defined for stereo matching on the next frame. The disparity can be estimated by minimizing this energy function. Experimental results show that the algorithm is able to estimate the disparity images in an accurate and temporally consistent fashion.

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“…Recently, highly accurate motion estimation [21] has been extended to contour-based segmentation [22] following a well known segmentation scheme [23]. The authors demonstrate that extending the motion estimator to edge detection in a variational framework leads to an increase in accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Variational Approach to Joint Denoising, Edge Detection and Motion Estimation

Telea

Preußer

Garbe

et al. 2006

Lecture Notes in Computer Science

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Abstract. The estimation of optical flow fields from image sequences is incorporated in a Mumford-Shah approach for image denoising and edge detection. Possibly noisy image sequences are considered as input and a piecewise smooth image intensity, a piecewise smooth motion field, and a joint discontinuity set are obtained as minimizers of the functional. The method simultaneously detects image edges and motion field discontinuities in a rigorous and robust way. It comes along with a natural multi-scale approximation that is closely related to the phase field approximation for edge detection by Ambrosio and Tortorelli. We present an implementation for 2D image sequences with finite elements in space and time. It leads to three linear systems of equations, which have to be iteratively in the minimization procedure. Numerical results underline the robustness of the presented approach and different applications are shown.

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High Accuracy Optical Flow Estimation Based on a Theory for Warping

Cited by 2,065 publications

References 23 publications

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Disparity Estimation in Stereo Sequences using Scene Flow

A Variational Approach to Joint Denoising, Edge Detection and Motion Estimation

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