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- W2022763603 abstract "Head-shoulder detection is widely used in many applications, and robust image descriptors are crucial to the detection performance. In this paper, by exploiting the second-order region covariance descriptor as a complement to widely-used histogram-based descriptors, we propose a new two-stage coarse-to-fine cascade framework to make full use of both types of descriptors for robust head-shoulder detection. Specifically, in the first stage, two histogram-based descriptors, i.e., local Histogram of Oriented Gradients (HOG) and histogram of Local Binary Pattern (LBP), are utilized by a Viola-Jones classifier to rapidly reject most non-head-shoulder candidate windows. In contrast, the second stage further boost the performance via multiple kernel learning on Riemannian manifold formed by Region Covariance Matrix (RCM), a second-order statistic descriptor with stronger discriminative power. Experimental results on a public dataset demonstrate that our method improves detection rate significantly with satisfactory detection speed." @default.
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- W2022763603 date "2014-08-01" @default.
- W2022763603 modified "2023-09-27" @default.
- W2022763603 title "Robust Head-Shoulder Detection Using a Two-Stage Cascade Framework" @default.
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- W2022763603 doi "https://doi.org/10.1109/icpr.2014.482" @default.
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