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Efficient feature extraction for highway traffic density classification

Dinani, M. A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/IranianMVIP.2015.7397494
  3. Publisher: IEEE Computer Society
  4. Abstract:
  5. Traffic density estimation is one of the most challenging problems in Intelligent Transportation Systems. In this paper, we estimate the traffic flow density based on classification. Various new efficient features are introduced for distinguishing between different traffic states, including number of key-points, edges of difference-image and moving edges. These features describe the traffic flow without any need to individual vehicles detection and tracking. We experiment our proposed approach on a standard database and some real videos from Tehran roads. The results show high accuracy performance of our method, even in changes of environmental conditions (e.g., lighting), by using efficient features. Duo to low computational cost, our proposed approach for traffic density estimation is applicable in real time applications
  6. Keywords:
  7. Traffic density classification ; Classification (of information) ; Computer vision ; Extraction ; Feature extraction ; Highway traffic control ; Intelligent systems ; Intelligent vehicle highway systems ; Support vector machines ; Transportation ; Vehicle locating systems ; Computational costs ; Difference images ; Environmental conditions ; Intelligent transportation systems ; Real-time application ; Traffic densities ; Traffic flow density ; Vehicles detection ; Image processing
  8. Source: 9th Iranian Conference on Machine Vision and Image Processing, 18 November 2015 through 19 November 2015 ; Volume 2016-February , 2015 , Pages 14-19 ; 21666776 (ISSN) ; 9781467385398 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7397494