Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters. (11th January 2018)

• Record Type: Journal Article
• Authors: Zhang, Xinyu; Gao, Hongbo; Xue, Chong; Zhao, Jianhui; Liu, Yuchao
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 1(2018:Jan./Feb.)
• Publication Date: 2018-01-11
• Subjects: Vehicle detection -- vehicle object tracking -- adaptive threshold segmentation -- histogram of gradient symmetric computation -- adaptive Kalman filter; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417749949
• Languages: English
• ISSNs: 1729-8806

Abstract

Intelligent transportation systems and safety driver-assistance systems are important research topics in the field of transportation and traffic management. This study investigates the key problems in front vehicle detection and tracking based on computer vision. A video of a driven vehicle on an urban structured road is used to predict the subsequent motion of the front vehicle. This study provides the following contributions. (1) A new adaptive threshold segmentation algorithm is presented in the image preprocessing phase. This algorithm is resistant to interference from complex environments. (2) Symmetric computation based on a traditional histogram of gradient (HOG) feature vector is added in the vehicle detection phase. Symmetric HOG feature with AdaBoost classification improves the detection rate of the target vehicle. (3) A motion model based on adaptive Kalman filter is established. Experiments show that the prediction of Kalman filter model provides a reliable region for eliminating the interference of shadows and sharply decreasing the missed rate.