Adaptive segmentation for gymnastic exercises based on change detection over multiresolution combined differences
A new adaptive segmentation strategy is proposed to segment gymnasts in sport sequences accurately. It is based on a Markov random fields (MRF) change detection analysis operating on a multiresolution combination of static and dynamic image differences. After a morphological analysis of the segmented masks, estimated motion information in the area of interest is incorporated to improve the efficiency of the segmentation process. Although presented in the particular context of gymnastic exercises, the new segmentation strategy could be applied to other applications where moving objects on a quasi-static background need to be segmented.