To perform static cardiac imaging, manual inspection of CINE images is currently necessary to detect a quiescent window within the cardiac cycle. We propose an automated system using two chained Deep Neural Networks to determine localized end-systolic and end-diastolic resting phases. The first network finds a region of interest (e.g. RCA, right or left atrium) and the second determines a quantitative motion curve for this region. Training and evaluation was performed on data from volunteers and patients acquired on different scanners and field strengths and a comparison to manually annotated resting phases showed accuracy in the range of 35ms.