Jifeng Shen

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Sample misalignment exerts an important influence on training a rapid and accurate human detector, and it is a difficult problem to tackle with due to human articulation or manual annotation errors. Multiple instances learning method is an effective tool to deal with this difficulty without manual correction. In this paper, firstly, we propose a variable(More)
In this paper, a novel supervised local high-order differential channel feature is proposed for fast pedestrian detection. This method is motivated by the recent successful use of filtering on the multiple channel maps, which can improve the performance. This method firstly compute the multiple channel maps for the input RGB image, and average pooling is(More)