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    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, 2024, Volume 1, Issue 1: 44-57

    Free to Read | Research Article | Feature Paper | 29 May 2024
    CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
    by
    IECE Author Ming Gao 1 *
    IECE Author 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: https://doi.org/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
    Funding
    This work was supported without any funding.
    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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    IECE Transactions on Emerging Topics in Artificial Intelligence

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