Pose estimation and recognition of ground vehicles in aerial reconnaissance imagery

Hermiston, E.J.; Booth, David T.; Foulkes, Stephen B.; Reno, A.L.

doi:10.1109/icpr.1998.711209

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…A fast and accurate algorithm to estimate pose of object in images is needed in missile guidance, visual reconnaissance, UAV auxiliary landing, automatic parking lot [2] [3], etc.. The pose of object provides a sound foundation for following recognition task, which can analyze and identify objects more conveniently and accurately.…”

Section: Introductionmentioning

confidence: 99%

Aircraft pose estimation based on polar function descriptor

Chao

Yan

2012

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

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The problem of aircraft pose estimating in a single image is analyzed. A novel method combined mathematical morphological algorithm and Radon transform is introduced to detect classical aircraft pose using the feature of shapes and regions. Silhouettes obtained by image segmentation are simplified by mathematical morphological algorithm, and Radon transform is used to do line detection for the simplified image. Tow axises of the aircraft are determined by the feature of fuselage and wings, and pose of the aircraft are estimated by the angle of the lines. Experimental results show the effectiveness of the algorithm for aircraft pose estimation in a gray level image.

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Section: Introductionmentioning

confidence: 99%

Aircraft pose estimation based on polar function descriptor

Chao

Yan

2012

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

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“…Vehicle recognition from oblique high resolution views has been addressed by several authors [2][7] [6]. Hoogs and Mundy [7] propose to use region and contour segmentation techniques and rely on dark regions of certain size and form, that may be a vehicle shadow, and on simple features like parallel contours, that some vehicles display in a variety of perspectives.…”

Section: Introductionmentioning

confidence: 99%

“…Viola and Wells [12] render object models and compare characteristic properties of the gray value function of the rendered graphic and the image using mutual information. Hermitson et al [6] utilize this approach to oblique vehicle recognition. Rendering requires assumptions about the lighting and surface properties of the model.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Decisions in Production Net An Example from Vehicle Recognition

Michaelsen¹,

Stilla²

2002

Lecture Notes in Computer Science

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A structural knowledge-based vehicle recognition method is modified yielding a new probabilistic foundation for the decisions. The method uses a pre-calculated set of hidden line projected views of articulated polyhedral models of the vehicles. Model view structures are set into correspondence with structures composed from edge lines in the image. The correspondence space is searched utilizing a 4D Houghtype accumulator. Probabilistic models of the background and the error in the measurements of the image structures lead to likelihood estimations that are used for the decision. The likelihood is propagated along the structure of the articulated model. The system is tested on a cluttered outdoor scene. To ensure anytime performance the recognition process is implemented in a data-driven production system.

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“…We propose a structural approach using a complete bottom-up part-of analysis. This approach competes with mutual information methods [4] and some quite similar but probabilistic methods based on generalized cylinders [1]. Since our approach leads to high computational effort we propose to use rather simple well scaling methods and structures.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Computational Effort for Structural 3D Vehicle Recognition

Michaelsen¹,

Stilla²

2000

Advances in Pattern Recognition

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A model based structural recognition approach is used for 3D detection and localization of vehicles. It is theoretically founded by syntactic pattern recognition using coordinate grammars and depicted by production nets. The computational effort significantly depends on certain tolerance parameters and the distribution of input data in the attribute domain. A brief theoretical survey of these interrelations is accompanied by comparing the performance on synthetic random data to the performance on data from different natural environments.

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Pose estimation and recognition of ground vehicles in aerial reconnaissance imagery

Cited by 6 publications

References 4 publications

Aircraft pose estimation based on polar function descriptor

Aircraft pose estimation based on polar function descriptor

Probabilistic Decisions in Production Net An Example from Vehicle Recognition

Assessing the Computational Effort for Structural 3D Vehicle Recognition

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