  Multi-modal systems are quite common in the context of human activity
recognition; widely used RGB-D sensors (Kinect is the most prominent example)
give access to parallel data streams, typically RGB images, depth data,
skeleton information. The richness of multimodal information has been largely
exploited in many works in the literature, while an analysis of their
effectiveness for incremental template updating has not been investigated so
far. This paper is aimed at defining a general framework for unsupervised
template updating in multi-modal systems, where the different data sources can
provide complementary information, increasing the effectiveness of the updating
procedure and reducing at the same time the probability of incorrect template
modifications.
