Real-time camera motion classification for content-based indexing and retrieval using templates

In this paper, a simple approach to camera motion analysis is proposed to index videos compressed using MPEG (Moving Picture Experts Group) −1, 2 in faster than real time. Specifically, this paper presents a template-matching algorithm to classify basic camera operations. The proposed approach involves 1) the construction of motion vector fields (MVFs) and filtering out noise; and 2) the sub-MVFs which come from dividing an MVF into several non-overlapping areas for the template matching of camera motions. A fine segmentation also can be obtained for a video, based on the homogeneity of the camera operation in each unit. The advantages of this method lie in real-time processing and robustness to noisy environments such as false motion vectors and object motions. The experimental results show that the proposed algorithm successfully classifies the camera movements.