We propose and investigate a paradigm for activity recognition, distinguishing the "on-going activity" recognition task (OGA) from that addressing "complete activities" (CA). The former starts from a time interval and aims to discover which activities are going on inside it. The latter, in turn, focuses on terminated activities and amounts to taking an external perspective on activities. We argue that this distinction is quite natural and the OGA task has a number of interesting properties; e.g., the possibility of reconstructing complete activities in terms of on-going ones, the avoidance of the thorny issue of activity segmentation, and a straightforward accommodation of complex activities, etc. Moreover, some plausible properties of the OGA task are discussed and then investigated in a classification study, addressing: the dependence of classification performance on the duration of time windows and its relationship with actional types (homogeneous vs. non-homogeneous activities), and on the assortments of features used. Three types of visual features are exploited, obtained from a data set that tries to balance the pros and cons of laboratory-based and naturalistic ones. The results provide partial confirmation to the hypothesis and point to relevant open issues for future work.
What Is Happening Now? - Detection of Activities of Daily Living from Simple Visual Features
Lepri, Bruno;Mana, Nadia;Cappelletti, Alessandro;Pianesi, Fabio;Zancanaro, Massimo
2010-01-01
Abstract
We propose and investigate a paradigm for activity recognition, distinguishing the "on-going activity" recognition task (OGA) from that addressing "complete activities" (CA). The former starts from a time interval and aims to discover which activities are going on inside it. The latter, in turn, focuses on terminated activities and amounts to taking an external perspective on activities. We argue that this distinction is quite natural and the OGA task has a number of interesting properties; e.g., the possibility of reconstructing complete activities in terms of on-going ones, the avoidance of the thorny issue of activity segmentation, and a straightforward accommodation of complex activities, etc. Moreover, some plausible properties of the OGA task are discussed and then investigated in a classification study, addressing: the dependence of classification performance on the duration of time windows and its relationship with actional types (homogeneous vs. non-homogeneous activities), and on the assortments of features used. Three types of visual features are exploited, obtained from a data set that tries to balance the pros and cons of laboratory-based and naturalistic ones. The results provide partial confirmation to the hypothesis and point to relevant open issues for future work.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
