Fast and viewpoint robust human detection in uncluttered environments
Paul Blondel, Alex Potelle, C. Pégard, Rogelio Lozano · 2014
Human detection is a very popular field of computer vision. Few works propose a solution for detecting people whatever the camera's viewpoint such as for UAV applications. In this context even state-of-the-art detectors can fail to detect people. We found that the Integral Channel Features detector (ICF) is inoperant in such a context. In this paper, we propose an approach to still benefit from the assets of the ICF while considerably extending the angular robustness during the detection. The main contributions of this work are: a new framework based on the Cluster Boosting Tree and the ICF detector for viewpoint robust human detection; a new training dataset for taking into account the human shape modifications occuring when the pitch angle of the camera changes. We showed that our detector (the PRD) is superior to the ICF for detecting people from complex viewpoints in uncluttered environments and that the computation time of the detector is real-time compatible.