Abstract: In order to detect gross subject movement in a video image in a way which is not sensitive to illumination change, for example illumination changes caused by movement of shadows or sunlight, spaced pairs of image frames are selected from a video sequence and sub-divided into cells, and spatial frequency analysis is performed in each cell. The magnitude of the spatial frequency components in corresponding cells in the two selected image frames are compared. If the number of cells with high magnitude difference is high then the video image is determined as containing gross subject movement whereas if the number of cells with high magnitude differences is low, the sequence is determined as not containing gross movement, though it may contain illumination changes or no or fine movement.

Abstract: In order to detect gross subject movement in a video image in a way which is not sensitive to illumination change, for example illumination changes caused by movement of shadows or sunlight, spaced pairs of image frames are selected from a video sequence and sub-divided into cells, and spatial frequency analysis is performed in each cell. The magnitude of the spatial frequency components in corresponding cells in the two selected image frames are compared. If the number of cells with high magnitude difference is high then the video image is determined as containing gross subject movement whereas if the number of cells with high magnitude differences is low, the sequence is determined as not containing gross movement, though it may contain illumination changes or no or fine movement.