Visual Tracking via Deep Feature Fusion and Correlation Filters
1
College of Computer and Information Science, Southwest University, Chongqing 400715, China
2
Faculty of Psychology, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3370; https://doi.org/10.3390/s20123370
Received: 29 April 2020
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Revised: 5 June 2020
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Accepted: 10 June 2020
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Published: 14 June 2020
(This article belongs to the Section Physical Sensors)
Visual tracking is a fundamental vision task that tries to figure out instances of several object classes from videos and images. It has attracted much attention for providing the basic semantic information for numerous applications. Over the past 10 years, visual tracking has made a great progress, but huge challenges still exist in many real-world applications. The facade of a target can be transformed significantly by pose changing, occlusion, and sudden movement, which possibly leads to a sudden target loss. This paper builds a hybrid tracker combining the deep feature method and correlation filter to solve this challenge, and verifies its powerful characteristics. Specifically, an effective visual tracking method is proposed to address the problem of low tracking accuracy due to the limitations of traditional artificial feature models, then rich hiearchical features of Convolutional Neural Networks are used to make the multi-layer features fusion improve the tracker learning accuracy. Finally, a large number of experiments are conducted on benchmark data sets OBT-100 and OBT-50, and show that our proposed algorithm is effective.
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MDPI and ACS Style
Xia, H.; Zhang, Y.; Yang, M.; Zhao, Y. Visual Tracking via Deep Feature Fusion and Correlation Filters. Sensors 2020, 20, 3370. https://doi.org/10.3390/s20123370
AMA Style
Xia H, Zhang Y, Yang M, Zhao Y. Visual Tracking via Deep Feature Fusion and Correlation Filters. Sensors. 2020; 20(12):3370. https://doi.org/10.3390/s20123370
Chicago/Turabian StyleXia, Haoran, Yuanping Zhang, Ming Yang, and Yufang Zhao. 2020. "Visual Tracking via Deep Feature Fusion and Correlation Filters" Sensors 20, no. 12: 3370. https://doi.org/10.3390/s20123370
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