4.5 Article

A monocular wide-field speed sensor inspired by the crabs' visual system for traffic analysis

Journal

BIOINSPIRATION & BIOMIMETICS
Volume 18, Issue 2, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.1088/1748-3190/acb393

Keywords

biologically inspired computer vision; speed measurement; intelligent transportation systems; fisheye camera; omnidirectional camera

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The development of visual sensors for traffic analysis can benefit from mimicking two aspects of the crab's visual system: panoramic vision and visual processing strategy for a flat world. By using omnidirectional cameras, simultaneous movement analysis of multiple objects over broad areas becomes possible, reducing the complications associated with traditional vision systems. The constraint of objects moving on the ground enables the calculation of 3D vehicle trajectories using a single camera without the need for binocular vision techniques. The proposed method using the crab's visual strategy shows promising results in estimating vehicle speed in urban environments.
The development of visual sensors for traffic analysis can benefit from mimicking two fundamental aspects of the visual system of crabs: their panoramic vision and their visual processing strategy adapted to a flat world. First, the use of omnidirectional cameras in urban environments allows for analyzing the simultaneous movement of many objects of interest over broad areas. This would reduce the costs and complications associated with infrastructure: installation, synchronization, maintenance, and operation of traditional vision systems that use multiple cameras with a limited field of view. Second, in urban traffic analysis, the objects of interest (e.g. vehicles and pedestrians) move on the ground surface. This constraint allows the calculation of the 3D trajectory of the vehicles using a single camera without the need to use binocular vision techniques.The main contribution of this work is to show that the strategy used by crabs to visually analyze their habitat (monocular omnidirectional vision with the assumption of a flat world ) is useful for developing a simple and effective method to estimate the speed of vehicles on long trajectories in urban environments. It is shown that the proposed method estimates the speed with a root mean squared error of 2.7 km h(-1).

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