Fast approach for efficient vehicle counting

Fast approach for efficient vehicle counting

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Systems for counting vehicles should be fast enough to be implemented in real-time situations. Most of the related work uses two stages for vehicle counting, vehicle detection and tracking, which increase the computational complexity. In this Letter, a fast and efficient approach for vehicle counting is proposed, where there is no need for the vehicle tracking step. A background model is created only for a narrow region, a line, in the video frames. The moving vehicles are detected as foreground objects while passing this narrow region. Morphological processes are applied to the extracted objects to enhance them and decrease the effects of vehicle occlusions. Finally, an efficient counting vehicles method is introduced employing only the extracted detection information. The experimental results performed on diverse videos show that the proposed method is fast and accurate. The average execution time per frame is 7.78 ms.


    1. 1)
      • 1. Yang, H., Qu, S.: ‘Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition’, IET Intell. Transp. Syst., 2017, 12, (1), pp. 7585.
    2. 2)
      • 2. Quesada, J., Rodriguez, P.: ‘Automatic vehicle counting method based on principal component pursuit background modeling’. IEEE Int. Conf. on. Image Processing (ICIP), Phoenix, AZ, USA, September 2016, pp. 38223826.
    3. 3)
    4. 4)
      • 4. Bouvie, C., Scharcanski, J., Barcellos, P., et al: ‘Tracking and counting vehicles in traffic video sequences using particle filtering’. IEEE Int. Instrumentation and Measurement Technology Conf. (I2MTC), Minneapolis, MN, USA, March 2013, pp. 812815.
    5. 5)
      • 5. Abdelwahab, M.A., Abdelwahab, M.M.: ‘A novel algorithm for vehicle detection and tracking in airborne videos’. IEEE Int. Symp. on Multimedia (ISM), Miami, FL, USA, December 2015, pp. 6568.
    6. 6)
      • 6. Guerrero-Gómez-Olmedo, R., López-Sastre, R., Maldonado-Bascón, S., et al: ‘Vehicle tracking by simultaneous detection and viewpoint estimation’. Int. Work-Conf. on the Interplay Between Natural and Artificial Computation, Mallorca, Spain, June 2013, pp. 306316.
    7. 7)
      • 7. Aly, S.A., Mamdouh, A., Abdelwahab, M.: ‘Vehicles detection and tracking in videos for very crowded scenes’. The 13th Conf. on Machine Vision Applications (MVA), Kyoto, Japan, May 2013, pp. 311314.

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