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Detecting People Carrying Objects based on an Optical Flow Motion Model
Citation key 1278Senst2011
Author Tobias Senst and Ruben Heras Evangelio and Thomas Sikora
Title of Book IEEE Workshop on Applications of Computer Vision (WACV)
Pages 301–306
Year 2011
Address Kona, USA
Month jan
Note IEEE Catalog Number: CFP11082-CDR ISBN: 978-1-4244-9495-8 DOI:10.1109/WACV.2011.5711518
Editor Eric Mortensen
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.
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