@inproceedings{67d9899ee81e485b9390609129c971be,
title = "Robust visual tracking based on L1 expanded template",
abstract = "Most video tracking algorithms including L1 tracker often fail to track correctly under adverse conditions such as object occlusion, disappearance, etc. To address this issue, we propose an improved L1 tracker algorithm called Tracker-2, based on what we call the expanded template which includes the reference template and trail template. The reference template keeps the original features of the target and prevents errors from being introduced by false tracking results with the template update, which leads to the deviation of the target. The trail template records the trail tracking results to avoid massive use of trivial templates which may result in the false detection of occlusion. The experimental results on a number of standard data sets have proved that our Tracker-2 approach is able to deal with the occlusion problem effectively while maintaining the advantages of L1 tracker.",
keywords = "L1 Tracker, Reference Template, Sparse Representation, Trail Template, Visual Tracking",
author = "Dansong Cheng and Yongqiang Zhang and Feng Tian and Daming Shi and Xiangfang Liu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th International Conference on Machine Learning and Cybernetics, ICMLC 2017 ; Conference date: 09-07-2017 Through 12-07-2017",
year = "2017",
month = nov,
day = "14",
doi = "10.1109/ICMLC.2017.8108954",
language = "英语",
series = "Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "397--403",
booktitle = "Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017",
address = "美国",
}