Remote photoplethysmography ( rPPG) is a video-based and the non-contact heart rate ( HR) detection technique that is susceptible to motion noise due to weak amplitudes of blood volume pulse signal that carries, making it challenging to accurately detect video-based HR values in motion scenarios. This article studies HR estimation based on a combination of chrominance ( CHROM) signals and joint blind source separation (JBSS). On the one hand, CHROM is applied to each facial region of interest (ROI) and timedelay operation is followed by constructing two multi-channel datasets, which can highlight quasi-periodic variables while suppressing irregular motion noises. On the other hand, JBSS technique is applied to the generated two datasets to extract the common underlying source component vectors (SCVs), where the one indicating BVP signal is selected and the HR is measured. The proposed method is evaluated on two public databases UBFC-RPPG and ECG-Fitness, and compared with several other typical methods. The results show that the method achieves the best performance of HR estimation during the dramatic sport situation, with HRmae = 9. 93 bpm, HRrmse = 16. 17 bpm and r = 0. 75. It provides a solution for the practical applications of the rPPG technology.