<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mirela Popa</style></author><author><style face="normal" font="default" size="100%">A.K. Koc</style></author><author><style face="normal" font="default" size="100%">L.J.M. Rothkrantz</style></author><author><style face="normal" font="default" size="100%">Caifeng Shan</style></author><author><style face="normal" font="default" size="100%">Pascal Wiggers</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">undefined</style></author><author><style face="normal" font="default" size="100%">Van Laerhoven, Kristof</style></author><author><style face="normal" font="default" size="100%">Gelissen, Jean</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Kinect Sensing of Shopping related Actions</style></title><secondary-title><style face="normal" font="default" size="100%">Constructing Ambient Intelligence: AmI 2011 Workshops</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Action Recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Kinect.</style></keyword><keyword><style  face="normal" font="default" size="100%">Shopping Behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Surveillance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2011</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Amsterdam, Netherlands</style></pub-location><abstract><style face="normal" font="default" size="100%">Surveillance systems in shopping malls or supermarkets are usually used for detecting abnormal behavior. We used the distributed video cameras system to design digital shopping assistants which assess the behavior of customers while shopping, detect when they need assistance, and offer their support in case there is a selling opportunity. In this paper we propose a system for analyzing human behavior patterns related to products interaction, such as browse through a set of products, examine, pick products, try on, interact with the shopping cart, and look for support by waiving one hand. We used the Kinect sensor to detect the silhouettes of people and extracted discriminative features for basic action detection. Next we analyzed different classification methods, statistical and also spatio-temporal ones, which capture relations between frames, features, and basic actions. By employing feature level fusion of appearance and movement information we obtained an accuracy of 80% for the mentioned six basic actions.</style></abstract></record></records></xml>