Segmentation has crucial applications in computer vision, pattern recognition, and the processing of digital information. Video segmentation is a problem that crops up in lots of different contexts, such VOD, DVR, e-learning, GIS, and other similar systems. Pixels are roughly separated using a membership grading system that relies on a way of expanding the basic area. Motion capture and clustering of video segmentation is a major hurdle to overcome when obtaining and storing video data. Fuzzy c-means clustering is used here to divide up a movie into its constituent parts. Fuzzy theory provides a framework for describing scene changes based on fuzzy judgements. Because the data space is partitioned appropriately by the membership functions. The suggested method achieves a high degree of accuracy at a low rate of error.