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

• Published in: Journal of sensors Vol. 2017; no. 2017; pp. 1 - 9
• Main Authors: Yaghoobi Ershadi, Nastaran, Menéndez, José Manuel
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017; Hindawi; Hindawi Limited
• Subjects: Algorithms; Arid regions; Automation; Cameras; Classification; Counting; Driving conditions; Engineering research; Headlights; International conferences; Kalman filters; Motion perception; Movement; Night; Risk management; Signal processing; Subtraction; Surveillance; Surveillance systems; Tracking; Traffic control; Traffic models; Traffic surveillance; Unmanned aerial vehicles; Vehicles; Vibration; Weather
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2017/3812301

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.