Robust Remote Heart Rate Estimation In Car Driving Environment

In this paper, we propose a novel framework for estimating driver's heart rate remotely under driving condition. First, region of interest is selected by landmark points derived from discriminative response map fitting. Feature signal is then extracted for heart rate estimation and refined by dynamic standard deviation check to eliminate the noisy segments. Finally, heart rate is estimated by power spectral analysis using temporal filtered signal. We test our framework on video database captured from real-world car driving condition. Experimental results validate that the proposed method achieves better results compared with the most prominent conventional methods.