Abstract
An effective and efficient feature selection method based on Gentle Adaboost (GAB) cascade and the Four Direction Feature (FDF), namely, MutualCascade, is proposed in this paper, which can be applied to the pedestrian detection problem in a single image. MutualCascade improves the classic method of cascade to remove irrelevant and redundant features. The mutual correlation coefficient is utilized as a criterion to determine whether a feature should be chosen or not. Experimental results show that the MutualCascade method is more efficient and effective than Voila and Jones’ cascade and some other Adaboost-based method, and is comparable with HOG-based methods. It also demonstrates a higher performance compared with the state-of-the-art methods.
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Chong, Y., Li, Q., Kuang, H., Zheng, CH. (2012). MutualCascade Method for Pedestrian Detection. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_41
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DOI: https://doi.org/10.1007/978-3-642-31837-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31836-8
Online ISBN: 978-3-642-31837-5
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