Single online visual object tracking with enhanced tracking and detection learning
Single online visual object tracking with enhanced tracking and detection learning
复制标题
具有增强跟踪和检测学习功能的单一在线视觉对象跟踪
DOI:
10.1007/s11042-018-6787-6
复制
发表时间:
2018-10
影响因子:
3.6
通讯作者:
Zheng Zhenxian
中科院分区:
文献类型:
--
作者:
Yi Yang;Luo Liping;Zheng Zhenxian
Single online visual object tracking has been an active research topic for its wide application on various tasks. In this paper, a new framework and related approaches are proposed to solve this problem consisting of enhanced tracking and detection learning. In the enhanced tracking part, an appearance model based on correlation filter with deep CNN features and a dynamic model using improved pyramid optical flow method are employed. Two models cooperate together to depict object appearance and capture target trajectory, which also contribute to provide training samples for detection learning. In the detection learning part, a cascade classifier and P-N learning scheme are employed to reinitialize tracking when model drift occurs. Data experiments on several challenging benchmarks show that the presented method is comparable to the state-of-the-art.
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