Real-time people detection in videos using geometrical features and adaptive boosting

Authors:
Pablo Julian Pedrocca;Mohand Saïd Allili
Affiliations:
Université du Québec en Outaouais, Département d'Informatique et d'Ingénierie, Gatineau, QC, Canada;Université du Québec en Outaouais, Département d'Informatique et d'Ingénierie, Gatineau, QC, Canada
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

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Abstract

In this paper, we propose a new approach for detecting people in video sequences based on geometrical features and AdaBoost learning. Unlike its predecessors, our approach uses features calculated directly from silhouettes produced by change detection algorithms. Moreover, feature analysis is done part by part for each silhouette, making our approach efficiently applicable for partially-occluded pedestrians and groups of people detection. Experiments on real-world videos showed us the performance of the proposed approach for real-time pedestrian detection.