A Hybrid Real-Time Vision-Based Person Detection Method

Abstract

[EN] In this paper, we introduce a hybrid real-time method for vision-based pedestrian detection made up by the sequential combination of two basic methods applied in a coarse to fine fashion. The proposed method aims to achieve an improved balance between detection accuracy and computational load by taking advantage of the strengths of these basic techniques. Haar-like features combined with Boosting techniques, which have been demonstrated to provide rapid but not accurate enough results in human detection, are used in the first stage to provide a preliminary candidate selection in the scene. Then, feature extraction and classification methods, which present high accuracy rates at expenses of a higher computational cost, are applied over boosting candidates providing the final prediction. Experimental results show that the proposed method performs effectively and efficiently, which supports its suitability for real applications.This work is supported by CASBLIP project 6-th FP\cite{RefCASBLIP}. The authors acknowledge the support of the Technological Institute of Optics, Colour and Imaging of Valencia - AIDO. Dr. Samuel Morillas acknowledges the support of Generalitat Valenciana under grant GVPRE/2008/257 and Universitat Politècnica de València under grant Primeros Proyetos de Investigación 13202. }Oliver Moll, J.; Albiol Colomer, A.; Morillas, S.; Peris Fajarnes, G. (2011). A Hybrid Real-Time Vision-Based Person Detection Method. Waves. 86-95. http://hdl.handle.net/10251/57676S869