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Sensors 2017, 17(3), 512; doi:10.3390/s17030512

Robust Scale Adaptive Tracking by Combining Correlation Filters with Sequential Monte Carlo

1,2,3,* , 1,2
,
1,2
and
1,2
1
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Fabrizio Lamberti
Received: 12 January 2017 / Revised: 25 February 2017 / Accepted: 27 February 2017 / Published: 4 March 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2182 KB, uploaded 4 March 2017]   |  

Abstract

A robust and efficient object tracking algorithm is required in a variety of computer vision applications. Although various modern trackers have impressive performance, some challenges such as occlusion and target scale variation are still intractable, especially in the complex scenarios. This paper proposes a robust scale adaptive tracking algorithm to predict target scale by a sequential Monte Carlo method and determine the target location by the correlation filter simultaneously. By analyzing the response map of the target region, the completeness of the target can be measured by the peak-to-sidelobe rate (PSR), i.e., the lower the PSR, the more likely the target is being occluded. A strict template update strategy is designed to accommodate the appearance change and avoid template corruption. If the occlusion occurs, a retained scheme is allowed and the tracker refrains from drifting away. Additionally, the feature integration is incorporated to guarantee the robustness of the proposed approach. The experimental results show that our method outperforms other state-of-the-art trackers in terms of both the distance precision and overlap precision on the publicly available TB-50 dataset. View Full-Text
Keywords: target tracking; sequential Monte Carlo framework; correlation filter; scale estimation; occlusion target tracking; sequential Monte Carlo framework; correlation filter; scale estimation; occlusion
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Ma, J.; Luo, H.; Hui, B.; Chang, Z. Robust Scale Adaptive Tracking by Combining Correlation Filters with Sequential Monte Carlo. Sensors 2017, 17, 512.

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