Efficient Camera Motion Characterization for MPEG Video Indexing

Abstract

A novel approach to camera motion analysis is proposed to index videos compressed in MPEG-1 or MPEG-2. Specifically, it fits the motion vectors in the MPEG stream into the two-dimensional affine model to detect basic camera operations automatically. The proposed approach involves (1) the construction of motion vector fields (MVFs) by normalizing the types of motion vectors and filtering out noise; and (2) the qualitative interpretation of camera motions from the estimated model parameters in two levels (frame and temporal segment). Fine segmentation can also be obtained for a video, based on the homogeneity of camera motion in each unit. The advantages of our method lie in its computational efficiency and robustness to noisy environments such as false motion vectors and object motion. The proposed approach is validated by an experiment with real compressed video sequences.