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3 October 1995 Hopfield neural network for TTC and heading direction estimation for obstacle avoidance systems in planar passive navigation
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Abstract
In this paper a method for the estimation of the heading direction and of the time-of-collision of a moving vehicle is presented. The assumption that the motion can be described as a prevalence of translation motion is used to reduce the optic flow equations to a linear version. In this case 2D motion field assumes a radial shape with vectors directions intersecting in a point called focus of expansion. In the presented method a sparse linear optic flow map is derived in the most relevant and reliable areas of the image. These estimations are then used to derive information about 3D motion of the vehicle. Results on synthetic and real time-varying sequence are presented.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gabriella Convertino, Antonella Branca, Ettore Stella, and Arcangelo Distante "Hopfield neural network for TTC and heading direction estimation for obstacle avoidance systems in planar passive navigation", Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); https://doi.org/10.1117/12.222680
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