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Proceedings Paper

Towards accurate localization: long- and short-term correlation filters for tracking
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Paper Abstract

Visual tracking is a challenging problem, especially using a single model. In this paper, we propose a discriminative correlation filter (DCF) based tracking approach that exploits both the long-term and short-term information of the target, named LSTDCF, to improve the tracking performance. In addition to a long-term filter learned through the whole sequence, a short-term filter is trained using only features extracted from most recent frames. The long-term filter tends to capture more semantics of the target as more frames are used for training. However, since the target may undergo large appearance changes, features extracted around the target in non-recent frames prevent the long-term filter from locating the target in the current frame accurately. In contrast, the short-term filter learns more spatial details of the target from recent frames but gets over-fitting easily. Thus the short-term filter is less robust to handle cluttered background and prone to drift. We take the advantage of both filters and fuse their response maps to make the final estimation. We evaluate our approach on a widely-used benchmark with 100 image sequences and achieve state-of-the-art results.

Paper Details

Date Published: 10 April 2018
PDF: 9 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061553 (10 April 2018); doi: 10.1117/12.2302950
Show Author Affiliations
Minglangjun Li, Xidian Univ. (China)
Chunna Tian, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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