8 December 2015 Building a robust vehicle detection and classification module
Author Affiliations +
Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98751J (2015) https://doi.org/10.1117/12.2228806
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Anton Grigoryev, Anton Grigoryev, Timur Khanipov, Timur Khanipov, Ivan Koptelov, Ivan Koptelov, Dmitry Bocharov, Dmitry Bocharov, Vassily Postnikov, Vassily Postnikov, Dmitry Nikolaev, Dmitry Nikolaev, } "Building a robust vehicle detection and classification module", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751J (8 December 2015); doi: 10.1117/12.2228806; https://doi.org/10.1117/12.2228806


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