Adjustable linear models for optic flow based obstacle avoidance |
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| Authors: | Manuela Chessa Fabio Solari Silvio P. Sabatini |
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| Affiliation: | 1. University of Illinois at Urbana-Champaign, 201 N. Goodwin Avenue, Urbana, IL 61801, USA;2. Inha University, 1103 High-tech Center, Yonghyun-dong 253, Nam-gu, Incheon, Republic of Korea;3. Samsung Research America - Silicon Valley, 75 West Plumeria Drive, San Jose, CA 95134, USA;1. College of Engineering and Computer Science, Computational Imaging Lab, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816, USA;2. Advanced Micro Devices, Quadrangle Blvd., Orlando, FL 32817, USA;3. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;4. Department of EECS, Computational Imaging Laboratory, University of Central Florida, Orlando, FL 32816, USA;1. Department of Physics, University of Sergipe, P.O. Box 353, 49100-000 São Cristóvão, SE, Brazil;2. College of Sciences, Chongqing University of Posts and Telecommunications, Chongqing 400065, People’s Republic of China;3. Institute of Physics, University of Tartu, W. Ostwald Str. 1, 50411 Tartu, Estonia;4. Institute of Physics, Jan Dlugosz University, Armii Krajowej 13/15, PL-42200 Czestochowa, Poland |
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| Abstract: | An original framework to recover the first-order spatial description of the optic flow is proposed. The approach is based on recursive filtering, and uses a set of linear models that dynamically adjust their properties on the basis of context information. These models are inspired by the experimental evidence about motion analysis in biological systems. By checking the presence of these models in the optic flow through a multiple model Kalman Filter, it is possible to compute the coefficients of the affine description and to use this information for estimating the motion of the observer as well as the three-dimensional orientation of the surfaces in some points of interest in the scene. In order to systematically validate the approach, a set of benchmarking sequences is used, and, finally, the proposed algorithm is successfully applied in real-world automotive situations. |
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| Keywords: | Motion interpretation Affine description Recursive filtering Kalman filter Time-to-contact Surface orientation Biologically inspired vision |
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