Glasgow, Scotland (1989). [4] S.N. Efstratiadis and A.K. Katsaggelos, âA Multiple- ... 1735-1745 (1980). [SI D.R. Walker and K.R. Rao, âImproved Pel-Recursive ...
M4S A MODEL-BASED PEL-RECURSIVE MOTION ESTIMATION ALGORITHM
Serafim N. Efstratiadis and Aggelos K. Katsaggelos Northwestern University Department of Electrical Enginerring and Computer Science The TechnologicalInstitute Evanston, Illinois 60208-3118
Abstract In this paper, a model-based approach to pel-recursive motion estimation is presented. The derivation of the algorithm is similar to the Wiener-based pel-recursive motion estimation algorithm. However, the proposed algorithm utilizes the spatiotemporal correlations in an image sequence by considering an autoregressive model for the motion compensated frames. Therefore, depending on the support of the AR model, the estimation is based on two or more consecutive frames. Pel-recursive motion estimation algorithms which appeared in the literature are special cases of the algorithm presented here. Various implementation issues such as adaptive regularization and modeling of the motion field are considered. Based on experiments with typical videoconferencing scenes, we concluded that the proposed algorithm performs better than the two-frame Wiener-based pel-recursive algorithm with respect to accuracy, robustness and smoothness of the velocity field.
1 Introduction Motion estimation of images sequences finds a wide variety of applications ranging from object tracking to spatio-temporal motion compensated image sequence filtering [5]. In the area of image sequence coding, in particular, with applications such as digital TV, video-conferencing and video-phone, bandwidth reduction can be achieved by utilizing the interframe redundancy, which can be characterized by the motion field [7]. A motion estimate, indicating the motion trajectory, can be used to compensate for movement, thereby reducing the prediction error and, consequently, the total amount of bits needed for the transmission or storage of the image sequence. A class of motion estimation techniques is represented by pel-recursive algorithms [6]. Pelrecursive methods allow the motion vectors to be determined at the receiver as well as at the transmitter, hence no motion vectors need to be transmitted. In addition, they provide directly, that is, without the need of spatial interpolation, motion vectors with sub-pixel accuracy. In this paper, a model-based pel-recursive Wiener motion estimation algorithm is presented. So far all motion estimation algorithms presented in the literature were based on two consecutive frames. The motion vector at the working pixel was obtained by minimizing the displaced difference between the current and 'This material is based on work supported in part by N A T O under Grant
the previous frame. The algorithm presented here utilizes the spatio-temporal correlations of the image sequence by considering an autoregressive (AR) model for the motion compensated frames. Then, an estimate of the motion vector is obtamed by minimizing a prediction error function, where the prediction is done through the AR model and the initial estimate of the motion vector. In order to achieve a stable and fast converging motion estimation, a Wiener solution is obtained using a causal spatial mask rather than one point. The Wiener-based algorithm of Biemond et al. [l]is a special case of the algorithm presented here. The algorithms of Cafforio and Rocca and Walker and Rao [8] are also related to the algorithms derived from a Wiener formulation [I]. In Sec. 2 the derivation of the model-based motion estimation algorithm is presented and various special cases are discussed. In Sec. 3 various important implementation issues are discussed. In Sec. 4 a number of experiments are presented, where the performance of the model-based algorithm is studied in the presence of noise and compared with the two frame algorithm of [l].Finally, the conclusions drawn from our experiments and future research topics are covered in Sec. 5 .
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A Model-Based Pel-Recursive Motion Estimation Algorithm
Let fk represent the intensity values at the current frame and assume that v previous frames, namely, fk-e, e = 1,... v, are e = 0,. . v, the intensity available. Let us denote by fk-i(rk-e), value at any given pixel which is located at the position rk-c at the (IC - t)-th frame. Vector r r k denotes the (m,n ) point on the rectangular raster of the current frame f k ( m , n ) , l
