Parametric video compression using appearance space

The novelty of the approach presented in this paper is the unique object-based video coding framework for videos obtained from a static camera. As opposed to most existing methods, the proposed method does not require explicit 2D or 3D models of objects and hence is general enough to satisfy the need for varying types of objects in the scene. The proposed system detects and tracks an object in the scene by learning the appearance model of each object online using nontraditional uniform norm based subspace. At the same time the object is coded using the projection coefficients to the orthonormal basis of the subspace learnt. The tracker incorporates a predictive framework based upon Kalman filter for predicting the five motion parameters. The proposed method shows substantially better compression than MPEG2 based coding with almost no additional complexity.