Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments

Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of...

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Veröffentlicht in:IEEE transactions on fuzzy systems Jg. 29; H. 1; S. 90 - 102
Hauptverfasser: Liu, Shuai, Wang, Shuai, Liu, Xinyu, Lin, Chin-Teng, Lv, Zhihan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial intelligence
Complex environment
Computer vision
Correlation
Domains
fuzzy detection
Fuzzy logic
Fuzzy sets
Fuzzy systems
Mathematical model
Object recognition
Optical tracking
OTB100
Real time
Target tracking
Tracking
visual tracking
Visualization
ISSN:1063-6706, 1941-0034
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Zusammenfassung:Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.3006520