Journal of Terahertz Science and Electronic Information Technology , Volume. 22, Issue 11, 1304(2024)

Adaptive tracking of moving targets in video sequences based on deep learning

LI Jiaqi
Author Affiliations
  • School of Film, Modern College of Northwest University, Xi'an Shaanxi 710130, China
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    In response to the issues of low tracking accuracy in video sequences due to factors such as appearance changes, background clutter, and severe occlusions, a novel two-stage adaptive tracking model is proposed. This model includes two phases: target detection and bounding box estimation. In the target detection phase, the model roughly locates the target; in the bounding box estimation phase, the exact position of the target is determined. To address the complexity of video scenes and the challenges of tracking small targets, multi-feature fusion technology is employed to construct a rich target representation. Experimental results show that compared with models such as Simple Online and Realtime Tracking (SORT), Tracktor++, FairMOT, and Transformer, this model demonstrates the best overall performance, effectively balancing the relationship between computational speed and tracking accuracy, and showing good potential for application.

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    LI Jiaqi. Adaptive tracking of moving targets in video sequences based on deep learning[J]. Journal of Terahertz Science and Electronic Information Technology , 2024, 22(11): 1304

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    Paper Information

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    Received: Mar. 31, 2024

    Accepted: Jan. 3, 2025

    Published Online: Jan. 3, 2025

    The Author Email:

    DOI:10.11805/tkyda2024179

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