ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

The scale variation, deformation and occlusion are the important reasons for model drift. In order to overcome the effect of model drift on robust tracking, a multiple model tracking algorithm based on object proposals was proposed. Firstly, as object proposals can reflect the general object material properties, the proposed tracker replaced traditional sliding sampling with object proposals to adapt the displacement and scale variation in the tracking process. And then, in order to enhance the object representation ability, the deep convolutional feature was used to characterize the target. During this process, although the previous size of object proposals may be different, the deep convolutional feature of each object proposal can be extracted quickly by a ROI pooling layer, and each object proposals feature had the same length, which can help to model training and further improve the robustness of the tracker. Lastly, the multi-models selection mechanism was used to undo past bad model updates by selecting the best tracking model, and adjusting the searching area can achieve object re-detection. These measures can inhibit the effect of model drift on robust tracking. In order to verify the superiority of the algorithm, the OTB 2013 benchmark and UAV 20L benchmark, and some classic contrast algorithms recently were used to evaluate the proposed tracker. The results showed that the proposed tracker achieved the best performance on precision and success rate, and the effect of model drift on robust tracking can be effectively suppressed.