In most imaging applications it is assumed that the object under investigation does not move during single image acquisition. This, however, might not be true in practice due to arbitrary movements of the patient caused by insufficient fixation or inherent organ motion in medical applications and, even in micro-CT applications, due to mechanical inaccuracies or a poor fixation of the specimen. Especially for long acquisition sequences, motion of the object is likely to occur. In the present work, different approaches for motion detection in CT raw data are compared. The proposed methods use nothing but the information contained in the sinogram, which therefore makes them generally applicable for motion detection in CT. The applicability of known methods based on Helgason-Ludwig consistency conditions will be discussed. Furthermore, three novel methods are presented to determine the projection angle at which movement occurred. Generally, local sinogram-based characteristics of the object movement are used. Therefore, they allow an intra-acquisition detection of the position of the motion artifacts in the sinogram. In fact, an important advantage of the proposed methods is their feasibility for realtime motion detection. Moreover, they require less computational cost and memory compared to other state of the art methods. Further, one of the novel methods can particularly be used for the correction of abrupt translational movements. One of the most promising attempts for motion correction is the data-driven motion correction (DDMC) methodology. Whose idea is to subdivide the projection data into motion free subsets and estimate the motion parameters between these subsets accordingly by using a partial reconstruction. Therefor time of movements must be correctly estimated. The proposed methods can be used as a preprocessing step of DDMC for determination of such time of movements.