Group(s):  Computer Vision 

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BibTeX:
@article{liuwoergoettermarkelic2012, author = {Liu, G. and Wörgötter, F. and Markelic, I.}, title = {Stochastic Lane Shape Estimation Using Local Image Descriptors}, pages = {13  21}, journal = {IEEE Transactions on Intelligent Transportation Systems}, year = {2012}, month = {07}, doi = 10.1109/TITS.2012.2205146}, abstract = In this paper, we present a novel measurement model for particlefilterbased lane shape estimation. Recently, the particle filter has been widely used to solve lane detection and tracking problems, due to its simplicity, robustness, and efficiency. The key part of the particle filter is the measurement model, which describes how well a generated hypothesis (a particle) fits current visual cues in the image. Previous methods often simply combine multiple visual cues in a likelihood function without considering the uncertainties of local visual cues and the accurate probability relationship between visual cues and the lane model. In contrast, this paper derives a new measurement model by utilizing multiple kernel density to precisely estimate this probability relationship. The uncertainties of local visual cues are considered and modeled by Gaussian kernels. Specifically, we use a linearparabolic model to describe the shape of lane boundaries on a topview image and a partitioned particle filter (PPF), integrating it with our novel measurement model to estimate lane shapes in consecutive frames. Finally, the robustness of the proposed algorithm with the new measurement model is demonstrated on the DRIVSCO data sets.}} 

Abstract: In this paper, we present a novel measurement model for particlefilterbased lane shape estimation. Recently, the particle filter has been widely used to solve lane detection and tracking problems, due to its simplicity, robustness, and efficiency. The key part of the particle filter is the measurement model, which describes how well a generated hypothesis (a particle) fits current visual cues in the image. Previous methods often simply combine multiple visual cues in a likelihood function without considering the uncertainties of local visual cues and the accurate probability relationship between visual cues and the lane model. In contrast, this paper derives a new measurement model by utilizing multiple kernel density to precisely estimate this probability relationship. The uncertainties of local visual cues are considered and modeled by Gaussian kernels. Specifically, we use a linearparabolic model to describe the shape of lane boundaries on a topview image and a partitioned particle filter (PPF), integrating it with our novel measurement model to estimate lane shapes in consecutive frames. Finally, the robustness of the proposed algorithm with the new measurement model is demonstrated on the DRIVSCO data sets.  
Review:  


BibTeX:
@article{liuwoergoettermarkelic2012a, author = {Liu, G. and Wörgötter, F. and Markelic, I.}, title = {SquareRoot SigmaPoint Information Filtering}, journal = {IEEE Transactions on Automatic Control}, year = {2012}, doi = 10.1109/TAC.2012.2193708}, abstract = The sigmapoint information filters employ a number of deterministic sigmapoints to calculate the mean and covariance of a random variable which undergoes a nonlinear transformation. These sigmapoints can be generated by the unscented transform or Stirlings interpolation, which corresponds to the unscented information filter (UIF) and the central difference information filter (CDIF) respectively. In this technical note, we develop the squareroot extensions of UIF and CDIF, which have better numerical properties than the original versions, e.g., improved numerical accuracy, double order precision and preservation of symmetry. We also show that the squareroot unscented information filter (SRUIF) might lose the positivedefiniteness due to the negative Cholesky update, whereas the squareroot central difference information filter (SRCDIF) has only posi tive Cholesky update. Therefore, the SRCDIF is preferable to the SRUIF concerning the numerical stability.}} 

Abstract: The sigmapoint information filters employ a number of deterministic sigmapoints to calculate the mean and covariance of a random variable which undergoes a nonlinear transformation. These sigmapoints can be generated by the unscented transform or Stirlings interpolation, which corresponds to the unscented information filter (UIF) and the central difference information filter (CDIF) respectively. In this technical note, we develop the squareroot extensions of UIF and CDIF, which have better numerical properties than the original versions, e.g., improved numerical accuracy, double order precision and preservation of symmetry. We also show that the squareroot unscented information filter (SRUIF) might lose the positivedefiniteness due to the negative Cholesky update, whereas the squareroot central difference information filter (SRCDIF) has only posi tive Cholesky update. Therefore, the SRCDIF is preferable to the SRUIF concerning the numerical stability.  
Review:  


BibTeX:
@inproceedings{liuwoergoettermarkelic2012b, author = {Liu, G. and Wörgötter, F. and Markelic, I.}, title = {The SquareRoot Unscented Information Filter for State Estimation and Sensor Fusion}, booktitle = {International Conference on Sensor Networks SENSORNETS}, year = {2012}, abstract = This paper presents a new recursive Bayesian estimation method, which is the squareroot unscented information filter (SRUIF). The unscented information filter (UIF) has been introduced recently for nonlinear system estimation and sensor fusion. In the UIF framework, a number of sigma points are sampled from the probability distribution of the prior state by the unscented transform and then propagated through the nonlinear dynamic function and measurement function. The new state is estimated from the propagated sigma points. In this way, the UIF can achieve higher estimation accuracies and faster convergence rates than the extended information filter (EIF). As the extension of the original UIF, we propose to use the squareroot of the covariance in the SRUIF instead of the full covariance in the UIF for estimation. The new SRUIF has better numerical properties than the original UIF, e.g., improved numerical accuracy, double order precision and preservation of symmetry.}} 

Abstract: This paper presents a new recursive Bayesian estimation method, which is the squareroot unscented information filter (SRUIF). The unscented information filter (UIF) has been introduced recently for nonlinear system estimation and sensor fusion. In the UIF framework, a number of sigma points are sampled from the probability distribution of the prior state by the unscented transform and then propagated through the nonlinear dynamic function and measurement function. The new state is estimated from the propagated sigma points. In this way, the UIF can achieve higher estimation accuracies and faster convergence rates than the extended information filter (EIF). As the extension of the original UIF, we propose to use the squareroot of the covariance in the SRUIF instead of the full covariance in the UIF for estimation. The new SRUIF has better numerical properties than the original UIF, e.g., improved numerical accuracy, double order precision and preservation of symmetry.  
Review:  


BibTeX:
@inproceedings{liuwoergoettermarkelic2011b, author = {Liu, G. and Wörgötter, F. and Markelic, I.}, title = {Nonlinear Estimation Using Central Difference Information Filter}, pages = {593596}, booktitle = {IEEE International Workshop on Statistical Signal Processing}, year = {2011}, volume= {2830}, doi = 10.1109/SSP.2011.5967768}, abstract = n this contribution, we introduce a new state estimation filter for nonlinear estimation and sensor fusion, which we call cen tral difference information filter CDIF. As we know, the ex tended information filter EIF has two shortcomings: one is the limited accuracy of the Taylor series linearization method, the other is the calculation of the Jacobians. These shortcom ings can be compensated by utilizing sigma point information filters SPIFs, e.g. and the unscented information filter UIF, which uses deterministic sigma points to approximate the distribution of Gaussian random variables and does not require the calculation of Jacobians. As an alternative to the UIF, the CDIF is derived by using Stirlings interpolation to generate sigma points in the SPIFs architecture, which uses less parameters, has lower computational cost and achieves the same accuracy as UIF. To demonstrate the performance of our al gorithm, a classic space vehicle reentry tracking simulation is used}} 

Abstract: n this contribution, we introduce a new state estimation filter for nonlinear estimation and sensor fusion, which we call cen tral difference information filter CDIF. As we know, the ex tended information filter EIF has two shortcomings: one is the limited accuracy of the Taylor series linearization method, the other is the calculation of the Jacobians. These shortcom ings can be compensated by utilizing sigma point information filters SPIFs, e.g. and the unscented information filter UIF, which uses deterministic sigma points to approximate the distribution of Gaussian random variables and does not require the calculation of Jacobians. As an alternative to the UIF, the CDIF is derived by using Stirlings interpolation to generate sigma points in the SPIFs architecture, which uses less parameters, has lower computational cost and achieves the same accuracy as UIF. To demonstrate the performance of our al gorithm, a classic space vehicle reentry tracking simulation is used  
Review:  


BibTeX:
@inproceedings{liuwoergoettermarkelic2011, author = {Liu, G. and Wörgötter, F. and Markelic, I.}, title = {Lane Shape Estimation Using a Partitioned Particle Filter for Autonomous Driving}, pages = {16271633}, booktitle = {IEEE International Conference on Robotics and Automation ICRA}, year = {2011}, volume= {913}, doi = 10.1109/ICRA.2011.5979753}, abstract = This paper presents a probabilistic algorithm for lane shape estimation in an urban environment which is important for example for driver assistance systems and autonomous driving. For the first time, we bring together the socalled Partitioned Particle filter, an improvement of the traditional Particle filter, and the linearparabolic lane model which alleviates many shortcomings of traditional lane models. The former improves the traditional Particle filter by subdividing the whole state space of particles into several subspaces and estimating those subspaces in a hierarchical structure, such that the number of particles for each subspace is flexible and the robustness of the whole system is increased. Furthermore, we introduce a new statistical observation model, an important part of the Particle filter, where we use multi kernel density to model the probability distribution of lane parameters. Our observation model considers not only color and position information as image cues, but also the image gradient. Our experimental results illustrate the robustness and efficiency of our algorithm even when confronted with challenging scenes}} 

Abstract: This paper presents a probabilistic algorithm for lane shape estimation in an urban environment which is important for example for driver assistance systems and autonomous driving. For the first time, we bring together the socalled Partitioned Particle filter, an improvement of the traditional Particle filter, and the linearparabolic lane model which alleviates many shortcomings of traditional lane models. The former improves the traditional Particle filter by subdividing the whole state space of particles into several subspaces and estimating those subspaces in a hierarchical structure, such that the number of particles for each subspace is flexible and the robustness of the whole system is increased. Furthermore, we introduce a new statistical observation model, an important part of the Particle filter, where we use multi kernel density to model the probability distribution of lane parameters. Our observation model considers not only color and position information as image cues, but also the image gradient. Our experimental results illustrate the robustness and efficiency of our algorithm even when confronted with challenging scenes  
Review:  


BibTeX:
@inproceedings{liuwoergoettermarkelic2011a, author = {Liu, G. and Wörgötter, F. and Markelic, I.}, title = {SquareRoot SigmaPoint Information Filter for Nonlinear Estimation and Sensor Fusion}, pages = {2945  2950}, booktitle = {IEEE Transactions on Automatic Control}, year = {2011}, volume= {57}, number = {11}, month = {11}, doi = 10.1109/TAC.2012.2193708}, abstract = The sigmapoint information filters employ a number of deterministic sigmapoints to calculate the mean and covariance of a random variable which undergoes a nonlinear transformation. These sigmapoints can be generated by the unscented transform or Stirlings interpolation, which corresponds to the unscented information filter (UIF) and the central difference information filter (CDIF) respectively. In this technical note, we develop the squareroot extensions of UIF and CDIF, which have better numerical properties than the original versions, e.g., improved numerical accuracy, double order precision and preservation of symmetry. We also show that the squareroot unscented information filter (SRUIF) might lose the positivedefiniteness due to the negative Cholesky update, whereas the squareroot central difference information filter (SRCDIF) has only positive Cholesky update. Therefore, the SRCDIF is preferable to the SRUIF concerning the numerical stability.}} 

Abstract: The sigmapoint information filters employ a number of deterministic sigmapoints to calculate the mean and covariance of a random variable which undergoes a nonlinear transformation. These sigmapoints can be generated by the unscented transform or Stirlings interpolation, which corresponds to the unscented information filter (UIF) and the central difference information filter (CDIF) respectively. In this technical note, we develop the squareroot extensions of UIF and CDIF, which have better numerical properties than the original versions, e.g., improved numerical accuracy, double order precision and preservation of symmetry. We also show that the squareroot unscented information filter (SRUIF) might lose the positivedefiniteness due to the negative Cholesky update, whereas the squareroot central difference information filter (SRCDIF) has only positive Cholesky update. Therefore, the SRCDIF is preferable to the SRUIF concerning the numerical stability.  
Review:  


BibTeX:
@inproceedings{liuwoergoettermarkelic2010, author = {Liu, G. and Wörgötter, F. and Markelic, I.}, title = {Combining Statistical Hough Transform and Particle Filter for robust lane detection and tracking}, pages = {993 997}, booktitle = {Intelligent Vehicles Symposium IV, 2010 IEEE}, year = {2010}, doi = 10.1109/IVS.2010.5548021}, abstract = Lane detection and tracking is still a challenging task. Here, we combine the recently introduced Statistical Hough transform SHT with a Particle Filter PF and show its application for robust lane tracking. SHT improves the standard Hough transform HT which was shown to work well for lane detection. We use the local descriptors of the SHT as measurement for the PF, and show how a new three kernel density based observation model can be modeled based on the SHT and used with the PF. The application of the former becomes feasible by the reduced computations achieved with the tracking algorithm. We demonstrate the use of the resulting algorithm for lane detection and tracking by applying it to images freed from the perspective effect achieved by applying Inverse Perspective Mapping IPM. The presented results show the robustness of the presented algorithm}} 

Abstract: Lane detection and tracking is still a challenging task. Here, we combine the recently introduced Statistical Hough transform SHT with a Particle Filter PF and show its application for robust lane tracking. SHT improves the standard Hough transform HT which was shown to work well for lane detection. We use the local descriptors of the SHT as measurement for the PF, and show how a new three kernel density based observation model can be modeled based on the SHT and used with the PF. The application of the former becomes feasible by the reduced computations achieved with the tracking algorithm. We demonstrate the use of the resulting algorithm for lane detection and tracking by applying it to images freed from the perspective effect achieved by applying Inverse Perspective Mapping IPM. The presented results show the robustness of the presented algorithm  
Review: 
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