Fast vehicle detection based on feature and real-time prediction

The vehicle identification is a key technology of vehicle automatic driving and assistance systems. This paper proposes a new fast vehicle detection method based on feature learning and real-time prediction by combining ARMA model and AdaBoost algorithm, which can be applied in car driver assistance systems for road detection and vehicle identification with a monocular camera. Experimental results show that our proposed algorithm can take the target´s prior information into account, and extend AdaBoost algorithm in the time dimension that improve the accuracy of real-time detection to be faster and more accurate than the existing methods.