This paper presents an efficient and robust approach to detect right ventricular landmark points in short axis cardiac MRI, based on multiscale HOG descriptor and random forest classifier. First, candidate landmark locations are determined using multiscale Harris corner detector. Multiscale HOG descriptor is then extracted at the candidate search locations. A probabilistic random forest classifier model is trained to discriminate landmark points from non-landmark regions. The landmark position is then estimated as the weighted average of the candidate locations where weights are computed from the probability scores derived from the classifier. Experimental result performed on an image set of 15 patients demonstrates the effectiveness of our proposed method with average error (Euclidean distance between the detected landmark and the manually annotated landmark points) of 5.06 pixels. Contrary to most existing approaches, our proposed method has minor dependency to prior segmentation of right ventricle, hence is less affected by plausible segmentation error.