Video object segmentation algorithm based on adaptive template updating and multi-feature fusion

In order to solve the problem that SiamMask cannot adapt to the change of target appearance and the lack of use of feature information leads to rough mask generation, this paper proposes a video object segmentation algorithm based on the adaptive template update and the multi-feature fusion. First of all, the algorithm adaptively updates the template using the segmentation results of each frame; secondly, the hybrid pooling module is used to enhance the features extracted in the fourth stage of the backbone network, and the enhanced features are fused with the rough mask; finally, the feature fusion module is used to refine the rough mask stage by stage, which can effectively combine the spliced features. Experimental results show that, compared with SiamMask, the performance of the proposed algorithm is significantly improved. On the DAVIS2016 data-set, the region similarity and contour similarity of this algorithm are 0.727 and 0.696, respectively, which is 1.0% and 1.8% higher than that of the benchmark algorithm, and the speed reaches 40.2 f/s. On the DAVIS2017 data-set, the region similarity and contour similarity of this algorithm are 0.567 and 0.615, respectively, which is 2.4% and 3.0% higher than that of the benchmark algorithm, and the speed reaches 42.6 f/s.