Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result. Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve state-of-the-art results. The code will be available at https://github.com/hqucv/siamrn

Multi view technology is a hot spot in research of the current display, multiple viewpoints of freedom stereo display technology not only can let the viewer's feel lifelike three-dimensional effect, still can make the viewer from wearing the aid equipment trouble, which makes more and more colleges and universities, research institutes and companies engaged in multiple viewpoints freedom stereo display technology and related content in the research and development. In this review paper, with the medicine image processing problems, we using computer image processing technology, based on depth image analysis, combined with the depth of the prospect of video segmentation technology figure extraction technology from a single viewpoint video to extract every frame image corresponding to the depth map. Aimed at the application of the foreground segmentation technology, summed up in three different modes of foreground segmentation method, subdivided into: analysis of geometrical perspective scene information method, build the scene depth model method and the boundary information to realize the depth map is used to extract method of three categories. Finally, the paper presents a summary of the conclusion of grating free stereoscopic display technology.