15 April 2014 Forward vehicle detection using cluster-based AdaBoost
Author Affiliations +
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, ∼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.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yeul-Min Baek, Yeul-Min Baek, Whoi-Yul Kim, Whoi-Yul Kim, } "Forward vehicle detection using cluster-based AdaBoost," Optical Engineering 53(10), 102103 (15 April 2014). https://doi.org/10.1117/1.OE.53.10.102103 . Submission:

Back to Top