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Automatically detecting vital signs in videos, such as the estimation ofheart and respiration rates, is a challenging research problem in computervision with important applications in the medical field. One of the keydifficulties in tackling this task is the lack of sufficient supervisedtraining data, which severely limits the use of powerful deep neural networks.In this paper we address this limitation through a novel deep learningapproach, in which a recurrent deep neural network is trained to detect vitalsigns in the infrared thermal domain from purely synthetic data. What is mostsurprising is that our novel method for synthetic training data generation isgeneral, relatively simple and uses almost no prior medical domain knowledge.Moreover, our system, which is trained in a purely automatic manner and needsno human annotation, also learns to predict the respiration or heart intensitysignal for each moment in time and to detect the region of interest that ismost relevant for the given task, e.g. the nose area in the case ofrespiration. We test the effectiveness of our proposed system on the recentLCAS dataset and obtain state-of-the-art results.

In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos