@INPROCEEDINGS{1278Senst2011,
	AUTHOR = {Tobias Senst and Ruben Heras Evangelio and Thomas Sikora},
	TITLE = {Detecting People Carrying Objects based on an Optical Flow Motion Model},
	BOOKTITLE = {IEEE Workshop on Applications of Computer Vision (WACV)},
	YEAR = {2011},
	MONTH = jan,
	EDITOR = {Eric Mortensen},
	PAGES = {301--306},
	ADDRESS = {Kona, USA},
	NOTE = {IEEE Catalog Number: CFP11082-CDR
ISBN: 978-1-4244-9495-8
DOI:10.1109/WACV.2011.5711518},
	PDF = {http://elvera.nue.tu-berlin.de/files/1278Senst2011.pdf},
	ABSTRACT = {Detecting people carrying objects is a commonly formulated
problem as a first step to monitor interactions between
people and objects. Recent work relies on a precise
foreground object segmentation, which is often difficult to
achieve in video surveillance sequences due to a bad contrast
of the foreground objects with the scene background,
abrupt changing light conditions and small camera vibrations.
In order to cope with these difficulties we propose
an approach based on motion statistics. Therefore we use a Gaussian mixture motion model (GMMM) and, based on that model, we define a novel speed and direction independent motion descriptor in order to detect carried baggage as those regions not fitting in the motion description model of an average walking person. The system was tested with the
public dataset PETS2006 and a more challenging dataset including abrupt lighting changes and bad color contrast and compared with existing systems, showing very promissing results.}
}