Respiratory Rate (RR) is a vital health indicator, especially in infant monitoring, where early detection of abnormalities or variabilities in RR is crucial. Traditionally, the respiratory rate is extracted using contact-based methods, which, although reliable, can be quite intrusive and stressful for long-term monitoring. This study explores the potential of real-time remote RR monitoring on inexpensive hardware, by comparing three motion-based methods of extracting RR from RGB-camera feed: Pixel Intensity Changes (PIC), Optical Flow (OF), and Eulerian Video Magnification (EVM). The three algorithms were benchmarked using the public AIR-125 dataset, which features videos of infants in various positions, with a focus on their accuracy and computational intensity. The results show that the PIC algorithm slightly outperformed the other two algorithms in both accuracy and computational complexity. However, none of the algorithms managed to replicate the performance of the study which initially proposed the dataset as a benchmark.