Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, the authors present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows the authors to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, the authors apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, the authors count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that the authors proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

• Record URL:
  • https://doi.org/10.3390/s19132848
• Record URL:
  • https://www.mdpi.com/1424-8220/19/13/2848
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • © 2019 Leonel Rosas-Arias et al.
• Authors:
  • Rosas-Arias, Leonel
  • Portillo-Portillo, Jose
  • Hernandez-Suarez, Aldo
  • Olivares-Mercado, Jesus
  • Sanchez-Perez, Gabriel
  • Toscano-Medina, Karina
  • Perez-Meana, Hector
  • Sandoval Orozco, Ana Lucila
  • García Villalba, Luis Javier
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 2848
• Serial:
  • Sensors
  • Volume: 19
  • Issue Number: 13
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Detection and identification technologies; Machine learning; Motion; Traffic counting; Traffic flow; Video imaging detectors
• Uncontrolled Terms: Principal component analysis
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01716569
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 16 2019 5:19PM