Abstract
| Language | English |
|---|---|
| Article number | 063025 |
| Number of pages | 10 |
| Journal | Journal of Electronic Imaging |
| Volume | 26 |
| Issue number | 6 |
| Early online date | 12 Dec 2017 |
| DOIs | |
| Publication status | E-pub ahead of print - 12 Dec 2017 |
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Keywords
- visual tracking
- feature subset
- decontaminate
- evolutionary algorithm
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Decontaminate feature for tracking : adaptive tracking via evolutionary feature subset. / Liu, Qiaoyuan; Wang, Yuru; Yin, Minghao; Ren, Jinchang; Li, Ruizhi.
In: Journal of Electronic Imaging, Vol. 26, No. 6, 063025, 12.12.2017.Research output: Contribution to journal › Article
TY - JOUR
T1 - Decontaminate feature for tracking
T2 - Journal of Electronic Imaging
AU - Liu, Qiaoyuan
AU - Wang, Yuru
AU - Yin, Minghao
AU - Ren, Jinchang
AU - Li, Ruizhi
N1 - Copyright 2017 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
PY - 2017/12/12
Y1 - 2017/12/12
N2 - Although various visual tracking algorithms have been proposed in the last 2-3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion et al. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy caused low efficiency and ambiguity caused poor performance. In this paper, an effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, “Curse of Dimensionality” has been avoided whilst the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.
AB - Although various visual tracking algorithms have been proposed in the last 2-3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion et al. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy caused low efficiency and ambiguity caused poor performance. In this paper, an effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, “Curse of Dimensionality” has been avoided whilst the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.
KW - visual tracking
KW - feature subset
KW - decontaminate
KW - evolutionary algorithm
UR - https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging
U2 - 10.1117/1.JEI.26.6.063025
DO - 10.1117/1.JEI.26.6.063025
M3 - Article
VL - 26
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
SN - 1017-9909
IS - 6
M1 - 063025
ER -