Robust Head-Shoulder Detection Using a Two-Stage Cascade Framework
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.