Authors:
Félix Polla
1
;
Hélène Laurent
2
and
Bruno Emile
1
Affiliations:
1
University of Orleans, Prisme Laboratory EA 4229, Orléans, France
;
2
INSA CVL, University of Orleans, Prisme Laboratory EA 4229, Bourges, France
Keyword(s):
Low Resolution Infrared Sensor, Motion History Image (MHI), Feature Selection, Action Recognition.
Abstract:
This article is made in the context of action recognition from infrared video footage for indoor installations.
The sensor we use has some peculiarities that make the acquired images very different from those of the
visible imagery. It is developed within the CoCAPS project in which our work takes place. In this context,
we propose a hierarchical model that takes an image set as input, segments it, constructs the corresponding
motion history image (MHI), extracts and selects characteristics that are then used by three classifiers for
activity recognition purposes. The proposed model presents promising results, notably compared to other
models extracted from deep learning literature. The dataset, designed for the CoCAPS project in collaboration
with industrial partners, targets office situations. Seven action classes are concerned, namely: no action,
restlessness, sitting down, standing up, turning on a seat, slow walking, fast walking.