TY - GEN
T1 - Counting pedestrians in bidirectional scenarios using zenithal depth images
AU - Vera, Pablo
AU - Zenteno, Daniel
AU - Salas, Joaquín
PY - 2013
Y1 - 2013
N2 - In this document, we describe a people counting system that can precisely detect people as they are seen from a zenithal depth camera pointing at the floor. In particular, we are interested in scenarios where there are two preferred directions of motion. In our framework, we detect people using a Support Vector Machine classifier, follow their trajectory by modeling the problem of matching observations between frames as a bipartite graph, and determine the direction of their motion with a bi-directional classifier. We include experimental evidence, from four different scenarios, for each major stage of our method.
AB - In this document, we describe a people counting system that can precisely detect people as they are seen from a zenithal depth camera pointing at the floor. In particular, we are interested in scenarios where there are two preferred directions of motion. In our framework, we detect people using a Support Vector Machine classifier, follow their trajectory by modeling the problem of matching observations between frames as a bipartite graph, and determine the direction of their motion with a bi-directional classifier. We include experimental evidence, from four different scenarios, for each major stage of our method.
UR - http://www.scopus.com/inward/record.url?scp=84888252387&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38989-4_9
DO - 10.1007/978-3-642-38989-4_9
M3 - Contribución a la conferencia
SN - 9783642389887
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 84
EP - 93
BT - Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings
T2 - 5th Mexican Conference on Pattern Recognition, MCPR 2013
Y2 - 26 June 2013 through 29 June 2013
ER -