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Soft Computing

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13-10-2018 | Methodologies and Application

Object tracking via dense SIFT features and low-rank representation

Authors: Yong Wang, Xinbin Luo, Lu Ding, Jingjing Wu

Published in: Soft Computing | Issue 20/2019

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Abstract

In this paper, we present a low-rank sparse tracking method which builds upon the particle filtering framework. The proposed method learns the local dense scale-invariant feature transform features corresponding to candidate samples jointly by exploiting the underlying sparse and low-rank constraints. Furthermore, the alternating direction method of multipliers method guarantees the optimization equation can be solved accurately and robustly. We evaluate our proposed tracking method against 9 state-of-the-art trackers on a set of 64 challenging sequences. Experimental results show that the proposed method performs favorably against state-of-the-art trackers in terms of accuracy.

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Title: Object tracking via dense SIFT features and low-rank representation
Authors: Yong Wang
Xinbin Luo
Lu Ding
Jingjing Wu
Publication date: 13-10-2018
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 20/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3571-5

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