@inproceedings{6f2817e41ba1484a977477b003e8373e,
title = "Long-term scale adaptive tracking with kernel correlation filters",
abstract = "Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.",
keywords = "correlation filters, long-term tracking, moving area detection, object re-detection",
author = "Yueren Wang and Hong Zhang and Lei Zhang and Yifan Yang and Mingui Sun",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; 9th International Conference on Graphic and Image Processing, ICGIP 2017 ; Conference date: 14-10-2017 Through 16-10-2017",
year = "2018",
doi = "10.1117/12.2303390",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Hui Yu and Junyu Dong",
booktitle = "Ninth International Conference on Graphic and Image Processing, ICGIP 2017",
address = "美国",
}