RGB-infrared fusion tracking algorithm based on Siamese network

2021 ◽  
Author(s):  
Ruyou Li ◽  
Wennan Cui ◽  
Zhiyong Wang ◽  
Jingjing Zhang
Keyword(s):  
Tracking Algorithm ◽  
Siamese Network ◽  
Fusion Tracking

Multi-feature fusion tracking algorithm based on generative compression network

2021 ◽  
Author(s):  
Peng Wang ◽  
Huitong Fu ◽  
Xiaoyan Li ◽  
Jia Guo ◽  
Zhigang Lv ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Tracking Algorithm ◽  
Fusion Tracking

Tracking Algorithm for Siamese Network Based on Target-Aware Feature Selection

2020 ◽  
Vol 40 (9) ◽  
pp. 0915003
Author(s):  
陈志旺 Chen Zhiwang ◽  
张忠新 Zhang Zhongxin ◽  
宋娟 Song Juan ◽  
罗红福 Luo Hongfu ◽  
彭勇 Peng Yong
Keyword(s):  
Feature Selection ◽  
Tracking Algorithm ◽  
Siamese Network

scholarly journals SIAMESE NETWORK COMBINED WITH ATTENTION MECHANISM FOR OBJECT TRACKING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1315-1322
Author(s):  
D. Zhang ◽  
J. Lv ◽  
Z. Cheng ◽  
Y. Bai ◽  
Y. Cao
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Network Model ◽  
Input Image ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Learning Object ◽  
Feature Maps ◽  
Siamese Network ◽  
Network Object

Abstract. After the development of deep learning object tracking methods in recent years, the fully convolutional siamese network object tracking algorithm SiamFC has become a more classic deep learning object tracking algorithm. In view of the problem that the accuracy of the tracking results of SiamFC will be reduced in the case of complex backgrounds, this paper introduces the attention mechanism based on the SiamFC, which performs channel and spatial weighting on the feature maps obtained by convolution of the input image. At the same time, the backbone network model of CNN in the algorithm is adjusted, then the siamese network combined with attention mechanism for object tracking is proposed. It can strengthen the effectiveness of the results of feature extraction and enhance the ability of the network model to discriminate targets. In this paper, the algorithm is tested on the OTB2015, VOT2016 and VOT2017 datasets, and compared with multiple object tracking algorithms. Experimental results show that the algorithm in this paper can better solve the complex background problem in object tracking, and has certain advantages compared with other algorithms.

Visual Tracking Algorithm Based on Online Feature Discrimination with Siamese Network

2019 ◽  
Vol 39 (9) ◽  
pp. 0915003
Author(s):  
仇祝令 Zhuling Qiu ◽  
查宇飞 Yufei Zha ◽  
朱鹏 Peng Zhu ◽  
吴敏 Min Wu
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm ◽  
Siamese Network
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