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

Nunes, U. M., Faria, D. R. and Peixoto, P. (2016). Human Activity Recognition using Max-Min Skeleton-based Features and Key Poses. IN: IEEE RO-MAN'16: Workshop on Behavior Adaptation, Interaction and Learning for Assistive Robotics (BAILAR). 2016-08-26 - 2016-08-31.

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

Divisions: Engineering & Applied Sciences
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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
Published Date: 2016-08-31
Authors: Nunes, U. M.
Faria, D. R. ( 0000-0002-2771-1713)
Peixoto, P.

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record