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Dynamic Spatiotemporal Information Interaction for Multidrone Single Object Tracking
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AI
DOI：10.1109/JIOT.2026.3659525.png)
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Abstract
En 中文
Multidrone single object tracking is a key technology in the Internet of Things (IoT)-based aerial sensing systems. However, existing methods often neglect both the temporal continuity of the object across frames and spatial complementarity from multidrone viewpoints, limiting their robustness in large-scale and dynamic IoT environments. To address these issues, this article proposes a DSTII-MDOT, where spatiotemporal information refers to the object’s feature evolution over time (temporal) and its complementary multiview representations (spatial). First, a dynamic temporal feature aggregation (DTFA) module is proposed, which captures the object’s motion and appearance variations by exploiting frame-to-frame differences, thereby ensuring feature continuity and stable predictions across frames in long-term tracking. Next, a spatial alignment method for cross-modal (text–image) semantic alignment (CMSA) is proposed. This method ensures the semantic alignment between visual features and textual descriptions by leveraging the contrastive language-image pretraining (CLIP) model and employs a multihead cross-attention mechanism to capture the Top-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> regions of interest within the search area. This enhances cross-drone collaboration and reduces redundant communications in the IoT networks. Finally, a wavelet frequency-domain feature refinement (WFDFR) module is proposed to enhance the texture features of the template image, effectively solving the problem of blurred texture features or missing details in complex scenes. The experimental results on the MDOT dataset demonstrate that the proposed DSTII-MDOT tracker surpasses existing advanced methods in both success rate (SR) and precision, validating the effectiveness and superiority of the proposed method. The code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JerryBryant24/DSTII-MDOT.</uri>
Keywords:
Internet of Things (IoT)
multidrone single object tracking
semantic alignment
spatiotemporal information fusion
wavelet transform
Journal
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8.9
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1.4W
Citations:
7.8W