PT Unknown
AU E. Bondi
   L. Sidenari
   Andrew Bagdanov
   Alberto del Bimbo
TI Real-time people counting from depth imagery of crowded environments
BT 11th IEEE International Conference on Advanced Video and Signal based Surveillance
PY 2014
BP 337
EP 342
DI 10.1109/AVSS.2014.6918691
AB In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications.
ER