期刊
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II
卷 10306, 期 -, 页码 225-234出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-59147-6_20
关键词
Foreground detection; Background modeling; Probabilistic self-organising maps; Background features
资金
- Ministry of Economy and Competitiveness of Spain [TIN2014-53465-R]
- Autonomous Government of Andalusia (Spain) [TIC-6213, TIC-657]
- Autonomous Government of Extremadura (Spain) [IB13113]
- European Regional Development Fund (ERDF)
- NVIDIA Corporation
Traffic monitoring is one of the most popular applications of automated video surveillance. Classification of the vehicles into types is important in order to provide the human traffic controllers with updated information about the characteristics of the traffic flow, which facilitates their decision making process. In this work, a video surveillance system is proposed to carry out such classification. First of all, a feature extraction process is carried out to obtain the most significant features of the detected vehicles. After that, a set of Growing Neural Gas neural networks is employed to determine their types. A qualitative and quantitative assessment of the proposal is carried out on a set of benchmark traffic video sequences, with favorable results.
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