Combining fast extracted edge descriptors and feature sharing for rapid object detection

Abstract

We mainly focus on feature sharing problem for object detection in cluttered scenes. The contributions are two-fold. First, a novel kind of edge/contour descriptors is presented and they serve as the basic features for sharing. Compared with HOGs (histograms of oriented gradients), the descriptors show the approximately equivalent efficiency while much less computational lost. Second, to exploit feature sharing techniques for object detection, a mathematical representation of shared features for "sliding-window" based object detection methods is given. Also with the newly defined shared features, a learning framework based on Real-Adaboost algorithm and a reusing framework based on look-up table are proposed. Experimental results show both the efficiency of proposed features and feature sharing method. © 2013 Springer-Verlag.