Abstract
A method is proposed in this paper for life detection and non-contact respiratory rate measurement in cluttered environments. Only an RGB video of the detection area is required. In the method, spatial filtering is firstly applied to each frame of the video for image denoising. Gray level compensation follows to compensate for the change of gray level caused by the environment light. Thirdly, the gray levels of each pixel over time are filtered separately by a low-pass filter. At last, the human is located and the respiratory rate is measured. Tests on a self-made dataset show that an accuracy of 76.7% is achieved by the proposed method, which is better than that of the Convolutional Neural Networks (30%) and the histogram of oriented gradients (3.3%).
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