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Proceedings Paper

A complementary tracking model with multiple features
Author(s): Peng Gao; Yipeng Ma; Chao Li; Ke Song; Fei Wang; Liyi Xiao
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Paper Abstract

Discriminative Correlation Filters based tracking algorithms exploiting conventional handcrafted features have achieved impressive results both in terms of accuracy and robustness. In this paper, to achieve an efficient tracking performance, we propose a novel visual tracking algorithm based on a complementary ensemble model with multiple features. Additionally, to improve tracking results and prevent targets drift, we introduce an effective fusion method by exploiting relative entropy to coalesce all basic response maps and get an optimal response. Furthermore, we suggest a simple but efficient update strategy to boost tracking performance. Comprehensive evaluations are conducted on two tracking benchmarks demonstrate and the experimental results demonstrate that our method is competitive with numerous state-of-the-art trackers. Our tracker achieves impressive performance with faster speed on these benchmarks.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083618 (29 October 2018); doi: 10.1117/12.2500635
Show Author Affiliations
Peng Gao, Harbin Institute of Technology (China)
Yipeng Ma, Harbin Institute of Technology (China)
Chao Li, Harbin Institute of Technology (China)
Ke Song, Harbin Institute of Technology (China)
Fei Wang, Harbin Institute of Technology (China)
Liyi Xiao, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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