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Journal of Sensors
Volume 2017 (2017), Article ID 3812301, 9 pages
Research Article

Vehicle Tracking and Counting System in Dusty Weather with Vibrating Camera Conditions

E.T.S. de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain

Correspondence should be addressed to Nastaran Yaghoobi Ershadi

Received 8 May 2017; Accepted 17 July 2017; Published 24 August 2017

Academic Editor: Yasuko Y. Maruo

Copyright © 2017 Nastaran Yaghoobi Ershadi and José Manuel Menéndez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles are a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.) or dusty weather in arid and semiarid regions or at night, among others. In this paper, we proposed a method to track and count vehicles in dusty weather with a vibrating camera. For this purpose, we used a background subtraction based strategy mixed with extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result. Our proposed method was tested on several video surveillance records in different conditions such as in dusty or foggy weather, with a vibrating camera, and on roads with medium-level traffic volumes. The results showed that the proposed method performed better than other previously published methods, including the Kalman filter or Gaussian model, in different traffic conditions.