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


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

Divisions: College of Engineering & Physical Sciences
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Event Title: IEEE RO-MAN'16: Workshop on Behavior Adaptation, Interaction and Learning for Assistive Robotics (BAILAR)
Event Type: Other
Event Dates: 2016-08-26 - 2016-08-31
Uncontrolled Keywords: Human Daily Activity Recognition, Random Forest, Max-Min Skeleton-based Features, Key Poses, Static and Dynamic Features
Last Modified: 27 Jun 2024 12:01
Date Deposited: 16 Feb 2018 14:40
PURE Output Type: Paper
Published Date: 2016-08-31
Accepted Date: 2016-08-30
Authors: Nunes, U. M.
Faria, D. R. (ORCID Profile 0000-0002-2771-1713)
Peixoto, P.



Version: Accepted Version

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