A real-time person detection method for moving cameras

Abstract

In this paper, we introduce an advanced 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. Boosting techniques in human detection, which have been demonstrated to provide rapid but not accurate enough results, 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. © 2009 Springer Berlin Heidelberg.