International Journal of Advanced Robotic Systems | |
Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters | |
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
【 预 览 】
Files | Size | Format | View |
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RO201910251976241ZK.pdf | 783KB | download |