MutualCascade method for pedestrian detection

Abstract An effective and efficient feature selection method based on Gentle Adaboost (GAB) cascade and the Four Direction Feature (FDF), namely, MutualCascade, which can be applied to the pedestrian detection problem in a single image, is proposed in this paper. 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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