In this paper, we propose a novel approach to detect abrupt camera viewpoint changes in edited video materials (movies, TV shows), to improve human activity recognition. The motivation for this work lies in the difficulty of correctly identifying actions in case of camera motion and viewpoint changes, because of the abrupt variations in the appearance model of the scene, which significantly deteriorate the continuity of the spatio-temporal features under investigation. To this aim, we compute the motion interchange pattern (MIP) for each pixel in a video, from which a feature descriptor is constructed for the entire frame. The change in camera viewpoint is achieved through the one-class SVM. We apply our detector on the TV human interaction dataset (TVHI). The experimental results show that our approach can distinguish the abrupt changes with a high accuracy, allowing for an improvement also in the activity recognition performance.