Fast and viewpoint robust human detection in uncluttered environments

Paul Blondel (1) , Alex Potelle (1) , Claude Pégard (1) , Rogelio Lozano (2)

1 MIS - Modélisation, Information et Systèmes - UR UPJV 4290
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

Human detection is a very popular field of com-puter 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 con-siderably extending the angular robustness during the detection. The main contributions of this work are: 1) a new framework based on the Cluster Boosting Tree and the ICF detector for viewpoint robust human detection, 2) 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.

Mots clés: machine learning; human detection; viewpoint robust; multi-viewpoint; supervised training

Domaines: Traitement du signal et de l'image [eess.SP]

Paul Blondel, Alex Potelle, Claude Pégard, Rogelio Lozano. Fast and viewpoint robust human detection in uncluttered environments. IEEE Visual Communications and Image Processing (VCIP 2014), Dec 2014, Valletta, Malta. pp.522-525. ⟨hal-01086137⟩