Imaging photoplethysmography (iPPG) has gained a lot of popularity as a contactless heart rate (HR) monitoring technique. However, most of the existing approaches to estimate HR are based on block-wise processing schemes, which are not optimal for real-time applications. The aim of this study was to investigate robust HR estimation methods having a short estimation delay, which would be suitable for real-time HR monitoring applications using iPPG. The three following algorithms were evaluated: 1) an algorithm based on adaptive sliding-window singular value decomposition (SWASVD), 2) an adaptive band-pass filter (OSC-ANF-W), 3) a notch-filter bank (NFB) estimation method. The database used to evaluate these algorithms was composed of 46 records, acquired in the light using an RGB camera or the dark using an NIR camera. The subjects were asked to perform different tasks to induce HR fluctuations. For the visible/dark sequences, average absolute errors (AAE) of 3.42/5.25, 3.14/4.21 and 3.98/6.02 bpm were obtained for the SWASVD, the OSC-ANF-W and the NFB algorithms, respectively. The corresponding averaged estimation delays were 4 seconds for the SWASVD and OSC-ANF-W, and 3 seconds for the NFB.