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This paper presents a new method to describe spatio-temporal relationsbetween objects and hands, to recognize both interactions and activities withinvideo demonstrations of manual tasks. The approach exploits Scene Graphs toextract key interaction features from image sequences while simultaneouslyencoding motion patterns and context. Additionally, the method introducesevent-based automatic video segmentation and clustering, which allow for thegrouping of similar events and detect if a monitored activity is executedcorrectly. The effectiveness of the approach was demonstrated in twomulti-subject experiments, showing the ability to recognize and clusterhand-object and object-object interactions without prior knowledge of theactivity, as well as matching the same activity performed by differentsubjects.

Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection