AdaBoost Learning for Human Detection Based on Histograms of Oriented Gradients

We developed a novel learning-based human detection system, which can detect people having different sizes and orientations, under a wide variety of backgrounds or even with crowds. To overcome the affects of geometric and rotational variations, the system automatically assigns the dominant orientations of each block-based feature encoding by using the rectangularand circulartype histograms of orientated gradients (HOG), which are insensitive to various lightings and noises at the outdoor environment. Moreover, this work demonstrated that Gaussian weight and tri-linear interpolation for HOG feature construction can increase detection performance. Particularly, a powerful feature selection algorithm, AdaBoost, is performed to automatically select a small set of discriminative HOG features with orientation information in order to achieve robust detection results. The overall computational time is further reduced significantly without any performance loss by using the cascade-ofrejecter structure, whose hyperplanes and weights of each stage are estimated by using the AdaBoost approach.