Learning target-aware background-suppressed correlation filters with dual regression for real-time UAV tracking

2021 ◽  
pp. 108352
Author(s):  
Fei Zhang ◽  
Shiping Ma ◽  
Zhuling Qiu ◽  
Tao Qi
Keyword(s):  
Real Time ◽  
Correlation Filters ◽  
Dual Regression

scholarly journals Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling

2015 ◽  
Author(s):  
Massimo Camplani ◽  
Sion Hannuna ◽  
Majid Mirmehdi ◽  
Dima Damen ◽  
Adeline Paiement ◽  
...  
Keyword(s):  
Real Time ◽  
Occlusion Handling ◽  
Correlation Filters

scholarly journals Real-Time Pattern Recognition with Adaptive Correlation Filters

10.5772/4977 ◽  
2007 ◽  
Author(s):  
Vitaly Kober ◽  
Victor H. ◽  
J. Angel ◽  
Josue Alvarez-Borrego
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Correlation Filters ◽  
Time Pattern

scholarly journals Real-Time Tracking Based on Keypoints and Discriminative Correlation Filters

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32745-32753 ◽  
Author(s):  
Chao Zheng ◽  
Zhenzhong Wei
Keyword(s):  
Real Time ◽  
Correlation Filters ◽  
Real Time Tracking

Real-time visual tracking using complementary kernel support correlation filters

2019 ◽  
Vol 14 (2) ◽  
pp. 417-429 ◽  
Author(s):  
Zhenyang Su ◽  
Jing Li ◽  
Jun Chang ◽  
Bo Du ◽  
Yafu Xiao
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Correlation Filters

scholarly journals Real-Time Visual Tracking with Variational Structure Attention Network

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4904 ◽  
Author(s):  
Yeongbin Kim ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Boundary Effect ◽  
Online Training ◽  
Correlation Filter ◽  
Shape Distortion ◽  
Correlation Filters ◽  
Attention Network ◽  
Real Time Processing ◽  
Variational Structure

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.

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