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Proceedings Paper

Building a robust vehicle detection and classification module
Author(s): Anton Grigoryev; Timur Khanipov; Ivan Koptelov; Dmitry Bocharov; Vassily Postnikov; Dmitry Nikolaev
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Paper Abstract

The growing adoption of intelligent transportation systems (ITS) and autonomous driving requires robust real-time solutions for various event and object detection problems. Most of real-world systems still cannot rely on computer vision algorithms and employ a wide range of costly additional hardware like LIDARs. In this paper we explore engineering challenges encountered in building a highly robust visual vehicle detection and classification module that works under broad range of environmental and road conditions. The resulting technology is competitive to traditional non-visual means of traffic monitoring. The main focus of the paper is on software and hardware architecture, algorithm selection and domain-specific heuristics that help the computer vision system avoid implausible answers.

Paper Details

Date Published: 8 December 2015
PDF: 7 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751J (8 December 2015); doi: 10.1117/12.2228806
Show Author Affiliations
Anton Grigoryev, Moscow Institute of Physics and Technology (Russian Federation)
Institute for Information Transmission Problems (Russian Federation)
Timur Khanipov, Institute for Information Transmission Problems (Russian Federation)
Ivan Koptelov, Institute for Information Transmission Problems (Russian Federation)
Dmitry Bocharov, Institute for Information Transmission Problems (Russian Federation)
Vassily Postnikov, Cognitive Technologies (Russian Federation)
Dmitry Nikolaev, Institute for Information Transmission Problems (Russian Federation)


Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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