Mr. Minglangjun Li Profile

Minglangjun Li

at Xidian Univ

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Proceedings Article | 10 April 2018 Paper

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

Minglangjun Li, Chunna Tian

Proceedings Volume 10615, 1061553 (2018) https://doi.org/10.1117/12.2302950

KEYWORDS: Image filtering, Optical tracking, Electronic filtering, Feature extraction, Video, Visual process modeling, Detection and tracking algorithms, Performance modeling, Statistical modeling, Binary data

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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.

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