Enhanced Cascading Classifier Using Multi-Scale HOG for Pedestrian Detection from Aerial Images

摘要

We propose a method for pedestrian detection from aerial images captured by unmanned aerial vehicles (UAVs). Aerial images are captured at considerably low resolution, and they are often subject to heavy noise and blur as a result of atmospheric influences. Furthermore, significant changes to the appearance of pedestrians frequently occur because of UAV motion. In order to address these crucial problems, we propose a cascading classifier that concatenates a pre-trained classifier and an online learning-based classifier. We construct the first classifier using deep belief network (DBN) with an extended input layer. Unlike previous approaches that use raw images as the input layer of the DBN, we exploit multi-scale histogram of oriented gradients (MSHOG) features. The MS-HOG enables us to supply better and richer information than low-resolution aerial images for constructing a reliable deep structure of DBN, because the dimensions of the input features can be expanded. Furthermore, the MS-HOG effectively extracts the necessary edge information while reducing trivial gradients and noise. The second classifier is based on online learning, and it uses predictions of the target appearance using UAV motions. Predicting the target appearance enables us to collect reliable training samples for the classifier's online learning process. Experiments using aerial videos demonstrate the effectiveness of the proposed method.