Real time people detection combining appearance and depth image spaces using boosted random ferns
Document typeConference report
Rights accessOpen Access
This paper presents a robust and real-time method for people detection in urban and crowed environments. Unlike other conventional methods which either focus on single features or compute multiple and independent classifiers specialized in a particular feature space, the proposed approach creates a synergic combination of appearance and depth cues in a unique classifier. The core of our method is a Boosted Random Ferns classifier that selects automatically the most discriminative local binary features for both the appearance and depth image spaces. Based on this classifier, a fast and robust people detector which maintains high detection rates in spite of environmental changes is created. The proposed method has been validated in a challenging RGB-D database of people in urban scenarios and has shown that outperforms state-of-the-art approaches in spite of the difficult environment conditions. As a result, this method is of special interest for real-time robotic applications where people detection is a key matter, such as human-robot interaction or safe navigation of mobile robots for example.
CitationVaquero, V., Villamizar, M.A., Sanfeliu, A. Real time people detection combining appearance and depth image spaces using boosted random ferns. A: Iberian Robotics Conference. "Robot 2015: Second Iberian Robotics Conference". Lisboa: Springer, 2015, p. 587-598.
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos 
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos 
- VIS - Visió Artificial i Sistemes Intel.ligents - Ponències/Comunicacions de congressos