Target tracking in infrared imagery using weighted composite reference function-based decision fusion

2006 ◽  
Vol 15 (2) ◽  
pp. 404-410 ◽  
Author(s):  
A. Dawoud ◽  
M.S. Alam ◽  
A. Bal ◽  
C. Loo
Keyword(s):  
Target Tracking ◽  
Decision Fusion ◽  
Infrared Imagery ◽  
Reference Function

Decision fusion algorithm for target tracking in forward-looking infrared imagery

2004 ◽  
Author(s):  
Amer Dawoud ◽  
Mohammad S. Alam ◽  
Abdullah Bal ◽  
Chey H. Loo
Keyword(s):  
Target Tracking ◽  
Decision Fusion ◽  
Infrared Imagery ◽  
Fusion Algorithm ◽  
Forward Looking

scholarly journals Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 10124-10145 ◽  
Author(s):  
Jiulu Gong ◽  
Guoliang Fan ◽  
Liangjiang Yu ◽  
Joseph Havlicek ◽  
Derong Chen ◽  
...  
Keyword(s):  
Target Tracking ◽  
Level Set ◽  
Infrared Imagery

Target tracking in airborne forward looking infrared imagery

2003 ◽  
Vol 21 (7) ◽  
pp. 623-635 ◽  
Author(s):  
Alper Yilmaz ◽  
Khurram Shafique ◽  
Mubarak Shah
Keyword(s):  
Target Tracking ◽  
Infrared Imagery ◽  
Forward Looking

Target tracking in infrared imagery using a novel particle filter

2009 ◽  
Vol 7 (7) ◽  
pp. 576-579 ◽  
Author(s):  
王芳林 Fanglin Wang ◽  
刘尔琦 Erqi Liu ◽  
杨杰 Jie Yang ◽  
郁生阳 Shengyang Yu ◽  
周越 Yue Zhou
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Infrared Imagery

Target Tracking Based on Adaptive Multilayer Convolutional Feature Decision Fusion

2020 ◽  
Vol 40 (23) ◽  
pp. 2315002
Author(s):  
陈法领 Chen Faling ◽  
丁庆海 Ding Qinghai ◽  
罗海波 Luo Haibo ◽  
惠斌 Hui Bin ◽  
常铮 Chang Zheng ◽  
...  
Keyword(s):  
Target Tracking ◽  
Decision Fusion

scholarly journals Learning to Track Aircraft in Infrared Imagery

2020 ◽  
Vol 12 (23) ◽  
pp. 3995
Author(s):  
Sijie Wu ◽  
Kai Zhang ◽  
Shaoyi Li ◽  
Jie Yan
Keyword(s):  
Target Tracking ◽  
Signal To Noise Ratio ◽  
Feature Learning ◽  
Tracking Algorithm ◽  
Correlation Filters ◽  
Infrared Imagery ◽  
Feature Representations ◽  
Domain Specific ◽  
Tightly Coupled ◽  
Airborne Target

Airborne target tracking in infrared imagery remains a challenging task. The airborne target usually has a low signal-to-noise ratio and shows different visual patterns. The features adopted in the visual tracking algorithm are usually deep features pre-trained on ImageNet, which are not tightly coupled with the current video domain and therefore might not be optimal for infrared target tracking. To this end, we propose a new approach to learn the domain-specific features, which can be adapted to the current video online without pre-training on a large datasets. Considering that only a few samples of the initial frame can be used for online training, general feature representations are encoded to the network for a better initialization. The feature learning module is flexible and can be integrated into tracking frameworks based on correlation filters to improve the baseline method. Experiments on airborne infrared imagery are conducted to demonstrate the effectiveness of our tracking algorithm.

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