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.

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

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