Variational Learning of Beta-Liouville Hidden Markov Models for Infrared Action Recognition

Samr Ali, Nizar Bouguila; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Infrared (IR) images are characterized by a lower sensitivity to lighting conditions than the visible spectrum. This opens the door to relatively untapped research potential of automatic recognition systems that are robust to shadows and variability in illumination levels or appearance. IR action recognition (AR) is one such application. It remains a fairly unexplored domain in IR. As such, in this paper, we propose the use of hidden Markov models (HMM) for IR AR. We also derive the mathematical model for the variational learning of Beta-Liouville (BL) HMMs. Next, we present the results of the proposed model on the Infrared Action Recognition (InfAR) dataset. To the best of our knowledge, this is the first application of HMMs to AR in the IR domain, and the first application of the BL HMMs to AR. Experimental results demonstrate promising results using different features extracted from the InfAR dataset.

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[bibtex]
@InProceedings{Ali_2019_CVPR_Workshops,
author = {Ali, Samr and Bouguila, Nizar},
title = {Variational Learning of Beta-Liouville Hidden Markov Models for Infrared Action Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}