In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.
|Title of host publication||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2015 Oct 14|
|Event||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States|
Duration: 2015 Jun 7 → 2015 Jun 12
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Other||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Period||15/6/7 → 15/6/12|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition