@inproceedings{546bce68fecd4cd5ad81d6591a719455,
title = "Self-adaptive visual tracker based on background information",
abstract = "Occlusion is a thorny issue in visual tracking, which may lead to serious drift to the tracking result. In this paper, a new tracker is proposed to deal with occlusion in tracking. Background surroundings to the object are divided into patches as supplementary information for occlusion detection. When the object is partially occluded, compensation will be made to the estimated position thus ensuring a better tracking result. The detector will be activated to search the whole frame for the object when object is missing. Correlation filter is applied to build the classifier for a higher speed and random ferns are used in the detector. Experiments are carried out on OTB benchmark videos and the result indicates the proposed tracker is preferable comparing to state-of-art trackers in handling occlusion.",
keywords = "Background information, Correlation filters, Object detection, Visual tracking",
author = "Shuqiao Sun and Wenjing Kang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2016 ; Conference date: 21-07-2016 Through 23-07-2016",
year = "2016",
month = dec,
day = "5",
doi = "10.1109/IMCCC.2016.205",
language = "英语",
series = "Proceedings - 2016 6th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1003--1008",
editor = "Junbao Li",
booktitle = "Proceedings - 2016 6th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2016",
address = "美国",
}