To solve the problems that most of the latest target trackers are faced with,such as nondiverse discriminate feature representation,coarse object locator,and limited quantities of positive samples,a tracking prediction learning algorithm based on multi-view multi-expert region proposal prediction algorithm was proposed.Multiple views and exploits powerful multiple sources of information were integrated,which solved nondiverse discriminate feature representation problem effectively.Multiple SVM classifier models were built on the expanded bounding boxes and the regional suggestion network module was added to accurately optimize it to predict optimal object location,which naturally alleviated the coarse object locator and limited quantities of positive samples problems at the same time.A comprehensive evaluation of the proposed approach on various video benchmark sequences was performed.The evaluation results demonstrate that the method proposed can significantly improve the tracking performance by combining the advantages of lightweight deep learning model and multi-view expert groups.
关键词
区域建议预测/特征判别机制/多专家组模型/多视图模型/特征融合/视觉跟踪/深度学习
Key words
region proposal prediction/feature discrimination/multi-expert groups model/multi-view model/feature fusion/visual tracking/deep learning
维普
万方数据