@article{pedrocca_real-time_2011,

title = {Real-time people detection in videos using geometrical features and adaptive boosting},

author = {P. J. Pedrocca and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960336661&doi=10.1007%2f978-3-642-21593-3_32&partnerID=40&md5=47ca975800e68648e02f76eba89a7457},

doi = {10.1007/978-3-642-21593-3_32},

issn = {03029743},

year  = {2011},

date = {2011-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {6753 LNCS},

number = {PART 1},

pages = {314–324},

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. © 2011 Springer-Verlag.},

note = {ISBN: 9783642215926 

Place: Burnaby, BC},

keywords = {Adaboost learning, Adaptive boosting, Change detection algorithms, Feature analysis, Feature extraction, Geometrical features, Geometry, Image analysis, Object recognition, Pedestrian detection, People detection, Real world videos, Signal detection, Video sequences},

pubstate = {published},

tppubtype = {article}

}