Abstract
Temporal segmentation and activity classification from first-person sensing
Highlights
Temporal segmentation of human motion into actions is central to the understanding and building computational models of human motion and activity recognition
In this work we explore the use of Inertial Measurement Units (IMUs) and a first-person camera for overall task classification, action segmentation and action classification in the context of cooking and preparing recipes in an unstructured environment
As a first step to exploring this space, we investigate the feasibility of standard supervised and unsupervised Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and K-Nearest Neighbor (K-NN) techniques for action segmentation and classification on these two modalities
Summary
Temporal segmentation of human motion into actions is central to the understanding and building computational models of human motion and activity recognition. Previous research has shown promising results, recognizing human activities and factorizing human motion into primitives and actions (i.e. temporal segmentation) is still an unsolved problem in human motion analysis. In this work we explore the use of Inertial Measurement Units (IMUs) and a first-person camera for overall task classification, action segmentation and action classification in the context of cooking and preparing recipes in an unstructured environment. This paper provides baseline results for recipe classification, action segmentation and action classification on the Carnegie Mellon University Multimodal Activity (CMUMMAC) database [6].
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