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In this paper, we present a novel algorithm for moving object detection and tracking. The proposed algorithm includes two schemes: one for spatio-temporal spatial segmentation and the other for temporal segmentation. A combination of these schemes is used to identify moving objects and to track them. A compound Markov random field (MRF) model is used as the prior image attribute model, which takes care of the spatial distribution of color, temporal color coherence and edge map in the temporal frames to obtain a spatio-temporal spatial segmentation. In this scheme, segmentation is considered as a pixel labeling problem and is solved using the maximum a posteriori probability (MAP) estimation technique. The MRF-MAP framework is computation intensive due to random initialization. To reduce this burden, we propose a change information based heuristic initialization technique. The scheme requires an initially segmented frame. For initial frame segmentation, compound MRF model is used to model attributes and MAP estimate is obtained by a hybrid algorithm [combination of both simulated annealing (SA) and iterative conditional mode (ICM)] that converges fast. For temporal segmentation, instead of using a gray level difference based change detection mask (CDM), we propose a CDM based on label difference of two frames. The proposed scheme resulted in less effect of silhouette. Further, a combination of both spatial and temporal segmentation process is used to detect the moving objects. Results of the proposed spatial segmentation approach are compared with those of JSEG method, and edgeless and edgebased approaches of segmentation. It is noticed that the proposed approach provides a better spatial segmentation compared to the other three methods. [ABSTRACT FROM AUTHOR]

This paper presents a statistical analysis of several key factors in H.264/AVC motion estimation, including the relationship between sum of absolute difference and sum of absolute transformed difference (SATD) in macroblock (MB) level, the changes of SATD from full-pixel precision to quarter-pixel precision, and so on. Based on these statistical characteristics, we proved the validity of a fractional motion estimation (FME) mode reduction criterion. During motion estimation, any MBs satisfying this criterion can keep their integer motion estimation best mode until after FME, thus avoiding many costly less probable prediction modes in the FME stage. Simulation results confirm that prediction schemes using this criterion have a very low miss rate in mode determination, FME speed-up is achieved and the peak signal-to-noise ratio drop is almost negligible. [ABSTRACT FROM PUBLISHER]