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- Title
Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters.
- Authors
Zhang, Xinyu; Gao, Hongbo; Xue, Chong; Zhao, Jianhui; Liu, Yuchao
- 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.
- Subjects
INTELLIGENT transportation systems; DRIVER assistance systems; KALMAN filtering; SPACE vehicle tracking; CHARGE coupled devices
- Publication
International Journal of Advanced Robotic Systems, 2018, Vol 15, Issue 1, p1
- ISSN
1729-8806
- Publication type
Article
- DOI
10.1177/1729881417749949