Multi-modal systems are quite common in the context of human activity recognition since widely used RGB-D sensors give access to parallel data streams (RGB, depth, skeleton). 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.
Franco A., Magnani A., Maio D. (2020). Template co-updating in multi-modal human activity recognition systems. Association for Computing Machinery [10.1145/3341105.3374085].
Template co-updating in multi-modal human activity recognition systems
Franco A.
;Magnani A.;Maio D.
2020
Abstract
Multi-modal systems are quite common in the context of human activity recognition since widely used RGB-D sensors give access to parallel data streams (RGB, depth, skeleton). 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
