In this paper we present a new descriptor for spatial distribution of motion activity in video sequences. We use the magnitude of the motion vectors as a measure of the intensity of motion cavity in a macro-block. We construct a matrix Cmv consisting of the magnitudes of the motion vector for each macro-block of a given P frame. We compute the average magnitude of the motion vector per macro-block Cavg, and then use Cavg as a threshold on the matrix C by setting the elements of C that are less than Cavg to zero. We classify the runs of zeros into three categories based on length, and count the number of runs of each category in the matrix C. Our activity descriptor for a frame thus consists of four parameters viz. the average magnitude of the motion vectors and the numbers of runs of short, medium and long length. Since the feature extraction is in the compressed domain and simple, it is extremely fast. We have tested it on the MPEG-7 test content set, which consists of approximately 14 hours of MPEG-1 encoded video content of different kinds. We find that our descriptor enables fast and accurate indexing of video. It is robust to noise and changes in encoding parameters such as frame size, frame rate, encoding bit rate, encoding format etc. It is a low-level non-semantic descriptor that gives semantic matches within the same program, and is thus very suitable for applications such as video program browsing. We also find that indirect and computationally simpler measures of the magnitude of the motion vectors such as bits taken to encode the motion vectors, though less effective, also can be used in our run-length framework.