Using Hough transform to define track boundaries as part of machine vision by train traffic-tracking hardware and software systems
https://doi.org/10.21683/1729-2646-2024-24-3-24-33
Abstract
Aim. In the context of machine vision, the problem of detected object boundary definition is usually solved using semantic segmentation that requires a high computational resource. Its application increases the complexity and the cost of the implemented hardware and software systems. This paper proposes an alternative method for defining the boundary of a segmented object, i.e., a railway track, for a train traffic tracking system. Methods. Since a railway track in an image can be represented by an n-th order polynomial, it is suggested to solve the problem of track boundary detection by using approximations in the form of straight lines. It is suggested using the Hough transform to detect straight lines. The former’s parametric space will be arranged in accordance with the problem to be solved. Conclusions. The proposed approximation will allow abandoning semantic segmentation and reduce the computational complexity and load.
About the Author
I. S. Polevskiy
Research and Design Institute for Information Technology, Signalling, and Telecommunications in Railway Transportation (JSC NIIAS)
Russian Federation
Russian Federation
Ilia S. Polevskiy, Chief Expert
27, bldg 1 Nizhegorodskaya St., Moscow, 109029
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Review
For citations:
Polevskiy I.S. Using Hough transform to define track boundaries as part of machine vision by train traffic-tracking hardware and software systems. Dependability. 2024;24(3):24-33. (In Russ.) https://doi.org/10.21683/1729-2646-2024-24-3-24-33
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ISSN 1729-2646 (Print)
ISSN 2500-3909 (Online)
ISSN 2500-3909 (Online)
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