Globally convergent autocalibration using interval analysis

Fusiello, Andrea; Benedetti, A.; Farenzena, Michela; Busti, A.

doi:10.1109/tpami.2004.125

Cited by 59 publications

(33 citation statements)

References 30 publications

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“…Telle and Ramdani [108] have utilized IA for camera calibration and 3D reconstruction, in which pixel coordinates were seen as two unknown but bounded variables (interval number), interval constraint propagation method was applied to propagate this uncertainties. IA can also be applied in auto-calibration [109], deformable image registration [110] and design of 2D image filter [111]. Considering the application of IA in the tracking framework (e.g., interval kernel function) can guarantee the truth-value of the tracking results and improve the robustness of tracking algorithm in autonomous driving.…”

Section: Conclusion and Feature Directionsmentioning

confidence: 99%

Review on Kernel based Target Tracking for Autonomous Driving

Wang

Yue

Dong

et al. 2016

Journal of Information Processing

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Significant progress has been made in the field of autonomous driving during the past decades. However, fully autonomous driving in urban traffic is still extremely difficult in the near future. Visual tracking of vehicles or pedestrians is an essential part of autonomous driving. Among these tracking methods, kernel-based object tracking is an effective means of tracking in video sequences. This paper reviews the kernel theory adopted in target tracking of autonomous driving and makes a qualitative and quantitative comparison among several well-known kernel based methods. The theoretical and experimental analysis allow us to conclude that the kernel based online subspace learning algorithm achieves a good trade-off between the stability and real-time processing for target tracking in the practical application environments of autonomous driving. This paper reports on the result of evaluating the performances of five algorithms by using seven video sequences.

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Section: Conclusion and Feature Directionsmentioning

confidence: 99%

Review on Kernel based Target Tracking for Autonomous Driving

Wang

Yue

Dong

et al. 2016

Journal of Information Processing

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show abstract

“…An interval analysis based branch and bound method for autocalibration is proposed in (Fusiello et al 2004), however the fundamental matrix based formulation does not scale well beyond a small number of views. Gröbner basis methods have been used to achieve optimal solutions for several geometric reconstruction problems, such as triangulation (Stewénius et al 2005), but they do not scale well for more than a very few number of views.…”

Section: Previous Workmentioning

confidence: 99%

Globally Optimal Algorithms for Stratified Autocalibration

Chandraker,

Agarwal,

Kriegman

et al. 2009

Int J Comput Vis

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We present practical algorithms for stratified autocalibration with theoretical guarantees of global optimality. Given a projective reconstruction, we first upgrade it to affine by estimating the position of the plane at infinity. The plane at infinity is computed by globally minimizing a least squares formulation of the modulus constraints. In the second stage, this affine reconstruction is upgraded to a metric one by globally minimizing the infinite homography relation to compute the dual image of the absolute conic (DIAC). The positive semidefiniteness of the DIAC is explicitly enforced as part of the optimization process, rather than as a post-processing step.For each stage, we construct and minimize tight convex relaxations of the highly non-convex objective functions in a branch and bound optimization framework. We exploit the inherent problem structure to restrict the search space for the DIAC and the plane at infinity to a small, fixed number of branching dimensions, independent of the number of views. Chirality constraints are incorporated into our convex relaxations to automatically select an initial region which is guaranteed to contain the global minimum. Experimental evidence of the accuracy, speed and scalability of our algorithm is presented on synthetic and real data.

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“…Other interesting methods are graph-cuts which have successfully been applied to multi-view stereo matching [19] and interval analysis applied to auto-calibration [5]. However, one of the drawbacks of [5], which is also true for many other global methods is that they are computationally highly demanding.…”

Section: Related Workmentioning

confidence: 99%

“…These points were used to compute inter-image homographies between consecutive views 5 . In Figure 4(a), the errors are shown.…”

Section: Real Datamentioning

confidence: 99%

Globally Optimal Estimates for Geometric Reconstruction Problems

Kahl

Henrion

2006

Int J Comput Vision

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We introduce a framework for computing statistically optimal estimates of geometric reconstruction problems. While traditional algorithms often suffer from either local minima or non-optimality -or a combination of both -we pursue the goal of achieving global solutions of the statistically optimal cost-function. Our approach is based on a hierarchy of convex relaxations to solve non-convex optimization problems with polynomials. These convex relaxations generate a monotone sequence of lower bounds and we show how one can detect whether the global optimum is attained at a given relaxation. The technique is applied to a number of classical vision problems

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Globally convergent autocalibration using interval analysis

Cited by 59 publications

References 30 publications

Review on Kernel based Target Tracking for Autonomous Driving

Review on Kernel based Target Tracking for Autonomous Driving

Globally Optimal Algorithms for Stratified Autocalibration

Globally Optimal Estimates for Geometric Reconstruction Problems

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