Human Activity Recognition using Max-Min Skeleton-based Features and Key Poses

U. M. Nunes, D. R. Faria, P. Peixoto

Research output: Contribution to conferencePaper

Abstract

Human activity recognition is still a very challenging
research area, due to the inherently complex temporal and
spatial patterns that characterize most human activities. This
paper proposes a human activity recognition framework based
on random forests, where each activity is classified requiring few
training examples (i.e. no frame-by-frame activity classification).
In a first approach, a simple mechanism that divides each action
sequence into a fixed-size window is employed, where max-min
skeleton-based features are extracted. In the second approach,
each window is delimited by a pair of automatically detected key
poses, where static and max-min dynamic features are extracted,
based on the determined activity example. Both approaches are
evaluated using the Cornell Activity Dataset [1], obtaining relevant
overall average results, considering that these approaches
are fast to train and require just a few training examples.
These characteristics suggest that the proposed framework can beuseful for real-time applications, where the activities are typicallywell distinctive and little training time is required, or to be
integrated in larger and sophisticated systems, for a first quick
impression/learning of certain activities
Original languageEnglish
Publication statusPublished - 31 Aug 2016
EventIEEE RO-MAN'16: Workshop on Behavior Adaptation, Interaction and Learning for Assistive Robotics (BAILAR) - New York, United States
Duration: 26 Aug 201631 Aug 2016

Conference

ConferenceIEEE RO-MAN'16: Workshop on Behavior Adaptation, Interaction and Learning for Assistive Robotics (BAILAR)
CountryUnited States
CityNew York
Period26/08/1631/08/16

Bibliographical note

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Keywords

  • Human Daily Activity Recognition, Random Forest, Max-Min Skeleton-based Features, Key Poses, Static and Dynamic Features

Cite this

Nunes, U. M., Faria, D. R., & Peixoto, P. (2016). Human Activity Recognition using Max-Min Skeleton-based Features and Key Poses. Paper presented at IEEE RO-MAN'16: Workshop on Behavior Adaptation, Interaction and Learning for Assistive Robotics (BAILAR), New York, United States.