Robust Local Optical Flow for Feature Tracking

Senst, Tobias; Eiselein, Volker; Sikora, Thomas

doi:10.1109/tcsvt.2012.2202070

Cited by 116 publications

(84 citation statements)

References 24 publications

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“…Despite the limitations of the quadratic penalty, this approach has become very popular for its implementation simplicity, low computational cost and available code in the OpenCV library [36,41]. However, robust estimation is often advocated [27,32,76,193,215] as mentioned in Section 3, especially for polynomial models, to deal with the frequent case of multiple motions in the estimation domain. Among the variety of optimization methods used in case of robust penalty function, the Iterative Reweighted Least Squares (IRLS) [119] and gradient descent approaches have mostly been used.…”

Section: Optimizationmentioning

confidence: 99%

“…Among the variety of optimization methods used in case of robust penalty function, the Iterative Reweighted Least Squares (IRLS) [119] and gradient descent approaches have mostly been used. IRLS proceeds by successive optimizations of quadratic problems weighted by a function of the current estimate and is implemented in the Motion2D software [193,215] with. Gradient descent approaches are often coupled with Graduated Non-Convexity (GNC) [27,33] to cope with non-convexity of (19).…”

Section: Optimizationmentioning

confidence: 99%

“…Multiple motions in a single patch can be partially handled with robust estimation by rejecting secondary motions, considered as outliers [27,95,193,215].…”

Section: Square Patchesmentioning

confidence: 99%

“…The size of the patch can be adapted with a bias-variance criterion [173], or based on a confidence measure on the reliability of the local domain for parametric estimation. Starting from a small patch size, it is thus possible to increase the size of the patch until the condition for a reliable domain is violated [173,215]. In the translational model case, one can analyze the singularity of the matrix M of the linear system (20) and, for example, impose a minimum threshold to the maximum eigenvalue of M [17].…”

Section: Square Patchesmentioning

confidence: 99%

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Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

369

182

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a b s t r a c tOptical flow estimation is one of the oldest and still most active research domains in computer vision. In 35 years, many methodological concepts have been introduced and have progressively improved performances, while opening the way to new challenges. In the last decade, the growing interest in evaluation benchmarks has stimulated a great amount of work. In this paper, we propose a survey of optical flow estimation classifying the main principles elaborated during this evolution, with a particular concern given to recent developments. It is conceived as a tutorial organizing in a comprehensive framework current approaches and practices. We give insights on the motivations, interests and limitations of modeling and optimization techniques, and we highlight similarities between methods to allow for a clear understanding of their behavior.

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Section: Optimizationmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

“…Multiple motions in a single patch can be partially handled with robust estimation by rejecting secondary motions, considered as outliers [27,95,193,215].…”

Section: Square Patchesmentioning

confidence: 99%

Section: Square Patchesmentioning

confidence: 99%

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Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

369

182

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show abstract

“…2 shows an example for the segmented image where each colour represents a homogeneous region, our assumption is that regions' discontinuity is the motion discontinuity. So that, rather than estimating optical flow for each pixel as in [23,24,25] or estimation a single optical flow for each block by assuming that all pixels in a certain block [26] have the same motion, we assume that motion of all pixels in each homogeneous region is the same. Therefore, we suggest modifying Eqs.…”

Section: B Segmentation-based Horn-schunck Algorithmmentioning

confidence: 99%

Resolution Enhancement by Incorporating Segmentation-based Optical Flow Estimation

Omer¹

2012

IJACSA

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Abstract-In this paper, the problem of recovering a highresolution frame from a sequence of low-resolution frames is considered. High-resolution reconstruction process highly depends on image registration step. Typical resolution enhancement techniques use global motion estimation technique. However, in general, video frames cannot be related through global motion due to the arbitrary individual pixel movement between frame pairs. To overcome this problem, we propose to employ segmentation-based optical flow estimation technique for motion estimation with a modified model for frame alignment. To do that, we incorporate the segmentation with the optical flow estimation in two-stage optical flow estimation. In the first stage, a reference image is segmented into homogeneous regions. In the second stage, the optical flow is estimated for each region rather than pixels or blocks. Then, the frame alignment is accomplished by optimizing the cost function that consists of L 1 -norm of the difference between the interpolated low-resolution (LR) frames and the simulated LR frames. The experimental results demonstrate that using segmentation-based optical flow estimation in motion estimation step with the modified alignment model works better than other motion models such as affine, and conventional optical flow motion models.

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Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

Sonnenschein

Ziel

2022

Preprint

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Forecasting of wastewater treatment plant inflow dynamics constitutes an enabler technology for wastewater treatment process optimization using model predictive control. However, accurate inflow prediction is still challenging, especially for strong rainfall events, where complex system dynamics and missing information on future rainfall represent limiting factors. We propose a seasonal probabilistic time series model for modelling the short-term wastewater inflow accurately while providing quantification of forecast uncertainty. To ensure suitability for practical implementation, the unconstrained parameters of the predictive distribution are modelled as linear functions of the input variables in the framework of Generalized Additive Models for Location Scale and Shape. Non-linear effects are approximated by Rectified Linear Units (ReLU), accounting for buffering within the sewer network and flow-dependent catchment response time. In addition to water level measurements from within the sewer network and rain rate measurements, rain forecasts are incorporated as exogenous regressors, where historical rain forecasts are used for model calibration. The model performance is evaluated on historical data from a German wastewater treatment plant using deterministic and probabilistic scoring rules. We benchmark against probabilistic time series models (SARX) and LSTMs. Our results show that the proposed model unites the benefits of high prediction accuracy of the LSTM and enhanced intelligibility of the SARX model, but accurate real-time rain forecasts are mandatory for successful real-world implementation.

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Robust Local Optical Flow for Feature Tracking

Cited by 116 publications

References 24 publications

Optical flow modeling and computation: A survey

Optical flow modeling and computation: A survey

Resolution Enhancement by Incorporating Segmentation-based Optical Flow Estimation

Probabilistic Intraday Wastewater Treatment Plant Inflow Forecast Utilizing Rain Forecast Data and Sewer Network Sensor Data

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