High performance object detection by collaborative learning of Joint Ranking of Granules features

Object detection remains an important but challenging task in computer vision. We present a method that combines high accuracy with high efficiency. We adopt simplified forms of APCF features, which we term Joint Ranking of Granules (JRoG) features; the features consists of discrete values by uniting binary ranking results of pair-wise granules in the image. We propose a novel collaborative learning method for JRoG features, which consists of a Simulated Annealing (SA) module and an incremental feature selection module. The two complementary modules collaborate to efficiently search the formidably large JRoG feature space for discriminative features, which are fed into a boosted cascade for object detection. To cope with occlusions in crowded environments, we employ the strategy of part based detection, as in but propose a new dynamic search method to improve the Bayesian combination of the part detection results. Experiments on several challenging data sets show that our approach achieves not only considerable improvement in detection accuracy but also major improvements in computational efficiency; on a Xeon 3GHz computer, with only a single thread, it can process a million scanning windows per second, sufficing for many practical real-time detection tasks.