Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-15-300
Title: Recognizing flu-like symptoms from videos
Authors: Thi T.H.
Wang L.
Ye N. 
Zhang J.
Maurer-Stroh S. 
Cheng L. 
Keywords: adult
algorithm
article
behavior
epidemic
epidemiological monitoring
female
human
influenza
male
medical informatics
methodology
middle aged
videorecording
young adult
Adult
Algorithms
Behavior
Disease Outbreaks
Epidemiological Monitoring
Female
Humans
Influenza, Human
Male
Medical Informatics
Middle Aged
Video Recording
Young Adult
Issue Date: 2014
Publisher: BioMed Central Ltd.
Citation: Thi T.H., Wang L., Ye N., Zhang J., Maurer-Stroh S., Cheng L. (2014). Recognizing flu-like symptoms from videos. BMC Bioinformatics 15 (1) : 300. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-15-300
Abstract: Background: Vision-based surveillance and monitoring is a potential alternative for early detection of respiratory disease outbreaks in urban areas complementing molecular diagnostics and hospital and doctor visit-based alert systems. Visible actions representing typical flu-like symptoms include sneeze and cough that are associated with changing patterns of hand to head distances, among others. The technical difficulties lie in the high complexity and large variation of those actions as well as numerous similar background actions such as scratching head, cell phone use, eating, drinking and so on. Results: In this paper, we make a first attempt at the challenging problem of recognizing flu-like symptoms from videos. Since there was no related dataset available, we created a new public health dataset for action recognition that includes two major flu-like symptom related actions (sneeze and cough) and a number of background actions. We also developed a suitable novel algorithm by introducing two types of Action Matching Kernels, where both types aim to integrate two aspects of local features, namely the space-time layout and the Bag-of-Words representations. In particular, we show that the Pyramid Match Kernel and Spatial Pyramid Matching are both special cases of our proposed kernels. Besides experimenting on standard testbed, the proposed algorithm is evaluated also on the new sneeze and cough set. Empirically, we observe that our approach achieves competitive performance compared to the state-of-the-arts, while recognition on the new public health dataset is shown to be a non-trivial task even with simple single person unobstructed view. Conclusions: Our sneeze and cough video dataset and newly developed action recognition algorithm is the first of its kind and aims to kick-start the field of action recognition of flu-like symptoms from videos. It will be challenging but necessary in future developments to consider more complex real-life scenario of detecting these actions simultaneously from multiple persons in possibly crowded environments. © 2014 Hue Thi et al.; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: https://scholarbank.nus.edu.sg/handle/10635/174299
ISSN: 14712105
DOI: 10.1186/1471-2105-15-300
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