Transformer Applications in Target Tracking
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 15 May 2025 | Viewed by 148
Special Issue Editors
2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071 , China
3. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 102401, China
Interests: image processing; artificial intelligence; deep learning; target detection; pattern recognition; target recognition
Interests: wireless resource allocation and management; wireless communications and networking; dynamic game and mean field game theory; big data analysis; security
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Special Issue Information
Dear Colleagues,
A convolutional neural networks is a neural network architecture for processing spatial data, such as images and videos. Given their good translational invariance and local perceptibility, they have been widely used in target classification and target tracking. However, CNNs cannot model long-range information, and they cannot effectively extract long-range feature information of the target to be tracked, which affects the efficiency and accuracy of target tracking. Since the release of transformer-based ChatGPT 3.0 on June 11, 2020, they have provided the powerful capability to process sequence data.
Although the CNN model has achieved great success in the field of target tracking over the years, there are still many problems in the practical application of the target tracking problem in complex scenes. This situation shows that there is a non-negligible gap between the theoretical progress in related fields and practical applications. Therefore, we invite papers on theoretical research and practical applications related to transformer architecture in the field of target tracking.
Dr. Fengping An
Dr. Haitao Xu
Dr. Chuyang Ye
Guest Editors
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Keywords
- transformers
- target tracking
- target recognition
- deep learning
- CNN
- sensors
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