Template co-updating in multi-modal human activity recognition systems

TL;DR

This paper proposes a general framework for unsupervised template updating in multi-modal human activity recognition systems, leveraging complementary data sources to improve accuracy and reduce errors in template modifications.

Contribution

It introduces a novel framework for incremental template updating in multi-modal systems, addressing the lack of prior analysis on effectiveness.

Findings

Framework enhances template updating accuracy

Utilizes complementary multi-modal data sources

Reduces incorrect template modifications

Abstract

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.