Remote photoplethysmography (PPG) estimates vital signs by measuring changes in the reflected light from the human skin. Compared to traditional PPG techniques, remote PPG enables contactless measurement at a reduced cost. In this paper, we propose a novel method to extract remote PPG signals and heart rate from videos. We propose an algorithm to dynamically track regions of interest (ROIs) and combine the signals from all ROIs based on signal qualities. To maintain a stable frame rate and accuracy, we propose a dynamic down-sampling approach, which makes our system robust to the different video resolutions and user-camera distances. We also propose the strategy of adaptive measurement time to estimate HR, which can achieve comparable accuracy in HR estimation while reducing the average measurement time. To test the accuracy of the proposed system, we have collected data from 30 subjects with facial masks. Experimental results show that the proposed system can achieve 3.0 bpm mean absolute error in HR estimation.