Journal
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II
Volume 10306, Issue -, Pages 225-234Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-59147-6_20
Keywords
Foreground detection; Background modeling; Probabilistic self-organising maps; Background features
Categories
Funding
- 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
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available