Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6949152 | ISPRS Journal of Photogrammetry and Remote Sensing | 2018 | 13 Pages |
Abstract
Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Xin Zhang, Gui-Song Xia, Qikai Lu, Weiming Shen, Liangpei Zhang,