@inproceedings{fa020a6dbee94cbc9d1b41be46a57e7a,
title = "Visual Tracking via Local Sparse Correlation Filters",
abstract = "Visual tracking is a challenging problem due to the intricate appearance variation of the objects in video sequences. Recently, correlation filters(CFs) technique has become a powerful tool for building a robust and high-speed visual tracker. However, there are still some intractable problems need to be solved: 1) The updating strategy of the CF's appearance model is linear, this strategy can not distinguish objects from the occlusions, may adding non-objects to the linear appearance model, 2) The conventional CFs can not handle the affine transforms of the objects. In this paper, we combine the local sparse method and CFs to construct an appearance model of the objects, and use the particle filters to find the objects' affine transforms. The experiments show that our approach outperforms the original local sparse coding approach and other state-of-the-art trackers.",
keywords = "correlation filters, sparse coding, visual tracking",
author = "Nana Fan and Xiao Ma and Zhenyu He and Yang, \{Wei Guo\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 3rd International Conference on Robot, Vision and Signal Processing, RVSP 2015 ; Conference date: 18-11-2015 Through 20-11-2015",
year = "2016",
month = feb,
day = "3",
doi = "10.1109/RVSP.2015.15",
language = "英语",
series = "Proceedings - 2015 3rd International Conference on Robot, Vision and Signal Processing, RVSP 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "27--30",
editor = "Xin-Hua Jiang and Tien-Szu Pan and Pei-Wei Tsai and Hsiang-Cheh Huang",
booktitle = "Proceedings - 2015 3rd International Conference on Robot, Vision and Signal Processing, RVSP 2015",
address = "美国",
}