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Open AccessArticle

LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation

1
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
2
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
3
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6853; https://doi.org/10.3390/s20236853
Received: 7 October 2020 / Revised: 15 November 2020 / Accepted: 26 November 2020 / Published: 30 November 2020
(This article belongs to the Special Issue Object Tracking and Motion Analysis)
In visual tracking, the tracking model must be updated online, which often leads to undesired inclusion of corrupted training samples, and hence inducing tracking failure. We present a locality preserving correlation filter (LPCF) integrating a novel and generic decontamination approach, which mitigates the model drift problem. Our decontamination approach maintains the local neighborhood feature points structures of the bounding box center. This proposed tracking-result validation approach models not only the spatial neighborhood relationship but also the topological structures of the bounding box center. Additionally, a closed-form solution to our approach is derived, which makes the tracking-result validation process could be accomplished in only milliseconds. Moreover, a dimensionality reduction strategy is introduced to improve the real-time performance of our translation estimation component. Comprehensive experiments are performed on OTB-2015, LASOT, TrackingNet. The experimental results show that our decontamination approach remarkably improves the overall performance by 6.2%, 12.6%, and 3%, meanwhile, our complete algorithm improves the baseline by 27.8%, 34.8%, and 15%. Finally, our tracker achieves the best performance among most existing decontamination trackers under the real-time requirement. View Full-Text
Keywords: object tracking; correlation filter; decontamination; model drift; locality preserving object tracking; correlation filter; decontamination; model drift; locality preserving
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MDPI and ACS Style

Zhou, Y.; Zhang, W.; Shi, Y.; Wang, Z.; Li, F.; Huang, Q. LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation. Sensors 2020, 20, 6853. https://doi.org/10.3390/s20236853

AMA Style

Zhou Y, Zhang W, Shi Y, Wang Z, Li F, Huang Q. LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation. Sensors. 2020; 20(23):6853. https://doi.org/10.3390/s20236853

Chicago/Turabian Style

Zhou, Yixuan; Zhang, Weimin; Shi, Yongliang; Wang, Ziyu; Li, Fangxing; Huang, Qiang. 2020. "LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation" Sensors 20, no. 23: 6853. https://doi.org/10.3390/s20236853

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