In the field of biomedical monitoring, Image Photoplethysmography (IPPG) enables contactless monitoring of resting heart rate (HR). However, while people are in motion such as head rotation, walking back and forth, or jogging in situ, the measurement accuracy of HR is susceptive to motion-induced signal distortion. In addition, in the scene of multi-people, how to accurately distinguish each person’s signal in real time is a critical issue. In this paper, a robust and real-time HR measurement system for multi-people using Open Computer Vision library (OpenCV) library is proposed, which mainly consists of five parts: face detection by feature points acquirement, a novel, fast yet simple face tracking, region of interest (ROI) adaptive extraction for increasing motion tolerance, signal processing for pulse extraction, and HR calculation via fast Fourier transform (FFT) under the double threads framework. Using Bland-Altman plots and Pearson’s correlation coefficient (CC), the HR estimated from videos recorded by a color CCD camera is compared to a figure blood volume pulse (BVP) senor to analyze agreement. The experiment results on 28 subjects show that the max average absolute error of HR estimation is less than 5 beats per minute (BPM), and that the CC is 0.910. In our case, the frame rate is 25 frames per second (FPS) for concomitant measurement of 7 subjects with a resolution of 1024×768 pixels. Overall, our HR measurement system for multi-people meets the requirements of accuracy, motion robustness, and real-time performance, and better extends the application range of IPPG technology.