Authors:
Thomas Moerland
;
Aswin Chandarr
;
Maja Rudinac
and
Pieter Jonker
Affiliation:
Technical University Delft, Netherlands
Keyword(s):
Action Recognition, Novelty Detection, Anomaly Detection, Computer Vision, Personal Robots.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
Novelty detection is essential for personal robots to continuously learn and adapt in open environments. This
paper specifically studies novelty detection in the context of action recognition. To detect unknown (novel)
human action sequences we propose a new method called background models, which is applicable to any
generative classifier. Our closed-set action recognition system consists of a new skeleton-based feature combined
with a Hidden Markov Model (HMM)-based generative classifier, which has shown good earlier results
in action recognition. Subsequently, novelty detection is approached from both a posterior likelihood and
hypothesis testing view, which is unified as background models. We investigate a diverse set of background
models: sum over competing models, filler models, flat models, anti-models, and some reweighted combinations.
Our standard recognition system has an inter-subject recognition accuracy of 96% on the Microsoft
Research Action 3D dataset. Moreover, the nov
elty detection module combining anti-models with flat models
has 78% accuracy in novelty detection, while maintaining 78% standard recognition accuracy as well.
Our methodology can increase robustness of any current HMM-based action recognition system against open
environments, and is a first step towards an incrementally learning system.
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