Building a robust vehicle detection and classification module

Anton Grigoryev; Timur Khanipov; Ivan Koptelov; Dmitry Bocharov; Vassily Postnikov; Dmitry Nikolaev

doi:10.1117/12.2228806

8 December 2015 Building a robust vehicle detection and classification module

Anton Grigoryev, Timur Khanipov, Ivan Koptelov, Dmitry Bocharov, Vassily Postnikov, Dmitry Nikolaev

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

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.

Citation Download Citation

Anton Grigoryev, Timur Khanipov, Ivan Koptelov, Dmitry Bocharov, Vassily Postnikov, and 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); https://doi.org/10.1117/12.2228806

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