Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters

International Journal of Advanced Robotic Systems

XinyuZhang¹ 

关键词: Vehicle detection;    vehicle object tracking;    adaptive threshold segmentation;    histogram of gradient symmetric computation;    adaptive Kalman filter;   

DOI  :  10.1177/1729881417749949

学科分类：自动化工程

来源: InTech

【 摘 要 】

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.

【 授权许可】

CC BY