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CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
Research Article | Published: 29 May 2024
IECE Transactions on Emerging Topics in Artificial Intelligence Volume 1, Issue 1, 2024: 44-57
Volume 1, Issue 1, IECE Transactions on Emerging Topics in Artificial Intelligence
Volume 1, Issue 1, 2024
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Guoxiong Zhou
Guoxiong Zhou
Central South University of Forestry and Technology, China
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IECE Transactions on Emerging Topics in Artificial Intelligence, Volume 1, Issue 1, 2024: 44-57

Open Access | Research Article | 29 May 2024
CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
by
Ming Gao Ming Gao 1 *
Shixin Yang Shixin Yang 2
1 School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
2 School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
* Corresponding Author: Ming Gao, [email protected]
DOI: 10.62762/TETAI.2024.240529
Received: 23 November 2023, Accepted: 21 May 2024, Published: 29 May 2024  
Cited by: 2  (Source: Google Scholar)
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Abstract
In the era of rapid technological advancement, the demand for sophisticated Multi-Object Tracking (MOT) systems in applications such as intelligent surveillance and autonomous navigation has become increasingly critical. However, existing models often struggle with accuracy and efficiency in densely populated or dynamically complex environments. Addressing these challenges, we introduce a novel deep learning-based MOT model that incorporates the latest CT-DETR detection technology and an advanced ReID module for improved pedestrian tracking. Experimental results demonstrate the model's superior performance in accurately identifying and tracking multiple targets across varied scenarios, significantly outperforming existing benchmarks. This research not only marks a significant leap forward in the field of video surveillance technology but also lays a foundational framework for future advancements in intelligent system applications, underscoring the importance of innovation in deep learning methodologies for real-world challenges.
Graphical Abstract
CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
Keywords
multi-object tracking
deep learning
CT-DETR
pedestrian re-identification
intelligent surveillance systems
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest. 
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Gao, M., & Yang, S. (2024). CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes. IECE Transactions on Emerging Topics in Artificial Intelligence, 1(1), 44–57. https://doi.org/10.62762/TETAI.2024.240529
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