Forward vehicle detection using cluster-based AdaBoost

A camera-based forward vehicle detection method with range estimation for forward collision warning system (FCWS) is presented. Previous vehicle detection methods that use conventional classifiers are not robust in a real driving environment because they lack the effectiveness of classifying vehicle samples with high intraclass variation and noise. Therefore, an improved AdaBoost, named cluster-based AdaBoost (C-AdaBoost), for classifying noisy samples along with a forward vehicle detection method are presented in this manuscript. The experiments performed consist of two parts: performance evaluations of C-AdaBoost and forward vehicle detection. The proposed C-AdaBoost shows better performance than conventional classification algorithms on the synthetic as well as various real-world datasets. In particular, when the dataset has more noisy samples, C-AdaBoost outperforms conventional classification algorithms. The proposed method is also tested with an experimental vehicle on a proving ground and on public roads, similar to 62 km in length. The proposed method shows a 97% average detection rate and requires only 9.7 ms per frame. The results show the reliability of the proposed method FCWS in terms of both detection rate and processing time. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)