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Edge-Graph Enhanced Network for Multi-Object Tracking in UAV Videos.

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Title: Edge-Graph Enhanced Network for Multi-Object Tracking in UAV Videos.
Authors: Xu, Yiming1 (AUTHOR), Ji, Hongbing1 (AUTHOR), Zhang, Yongquan1 (AUTHOR) zhangyq@xidian.edu.cn
Source: Remote Sensing. Mar2026, Vol. 18 Issue 6, p936. 30p.
Subjects: Multiple target tracking, Drone surveillance, Object recognition (Computer vision)
Abstract: Highlights: What are the main findings? An Edge-Graph Enhanced Network (EGEN) is proposed for robust multi-object tracking in UAV aerial videos, effectively addressing the challenges of small object scale, weak appearance cues, and complex background interference through edge-aware detection enhancement and graph-guided identity modeling. Two dedicated modules are designed: the Edge-Guided Gaussian Enhancement Module (EGGEM) for small object detection and the Graph-Guided Embedding Enhancement Module (GGEEM) for identity-discriminative embedding learning, together with a hierarchical two-stage data association strategy to ensure stable and accurate tracking. What are the implications of the main findings? The Edge-Guided Gaussian Enhancement Module (EGGEM) explicitly models global edge relationships between objects and background, guiding selective feature enhancement to strengthen key structural characteristics of small objects while suppressing background interference. This addresses the limitations of conventional implicit saliency-driven attention, effectively enhancing detection discriminability in complex UAV scenarios. The Graph-Guided Embedding Enhancement Module (GGEEM) overcomes the implicit nature of conventional context modeling by explicitly representing re-identification (ReID) embeddings as a graph. By explicitly representing and propagating relational information between objects, it effectively propagates identity information across objects, thereby enhancing tracking robustness. Multi-Object Tracking (MOT) is a fundamental research topic in the field of computer vision, with broad application potential in unmanned aerial vehicle (UAV) videos. However, existing methods still face significant challenges in detection discriminability and identity association stability due to the small scale and weak appearance of objects under aerial viewpoints, as well as complex background interference. To address these issues, we propose an Edge-Graph Enhanced Network (EGEN) for UAV aerial MOT, aiming to improve the performance of small object detection (SOD) and tracking in complex scenes. The framework follows a one-step tracking paradigm and consists of three main components: object detection, embedding feature extraction, and data association. In the detection stage, we design an Edge-Guided Gaussian Enhancement Module (EGGEM), which models edge relationships between objects and backgrounds from a global perspective and selectively enhances Gaussian features guided by edge information, thereby strengthening key structural features of small objects while suppressing background interference. In the embedding feature extraction stage, we develop a Graph-Guided Embedding Enhancement Module (GGEEM), which explicitly represents re-identification (ReID) embeddings as a graph structure and jointly models nodes and their neighborhood relationships to fully capture inter-object associations and enhance embedding discriminability. In the data association stage, we introduce a hierarchical two-stage association strategy to match objects with different confidence levels separately, improving tracking stability and robustness. Extensive experiments on the VisDrone, UAVDT, and self-constructed WildDrone datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in both SOD and MOT, demonstrating strong generalization and practical applicability. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Edge-Graph Enhanced Network for Multi-Object Tracking in UAV Videos.
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  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Yiming%22">Xu, Yiming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ji%2C+Hongbing%22">Ji, Hongbing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yongquan%22">Zhang, Yongquan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangyq@xidian.edu.cn</i>
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  Data: Highlights: What are the main findings? An Edge-Graph Enhanced Network (EGEN) is proposed for robust multi-object tracking in UAV aerial videos, effectively addressing the challenges of small object scale, weak appearance cues, and complex background interference through edge-aware detection enhancement and graph-guided identity modeling. Two dedicated modules are designed: the Edge-Guided Gaussian Enhancement Module (EGGEM) for small object detection and the Graph-Guided Embedding Enhancement Module (GGEEM) for identity-discriminative embedding learning, together with a hierarchical two-stage data association strategy to ensure stable and accurate tracking. What are the implications of the main findings? The Edge-Guided Gaussian Enhancement Module (EGGEM) explicitly models global edge relationships between objects and background, guiding selective feature enhancement to strengthen key structural characteristics of small objects while suppressing background interference. This addresses the limitations of conventional implicit saliency-driven attention, effectively enhancing detection discriminability in complex UAV scenarios. The Graph-Guided Embedding Enhancement Module (GGEEM) overcomes the implicit nature of conventional context modeling by explicitly representing re-identification (ReID) embeddings as a graph. By explicitly representing and propagating relational information between objects, it effectively propagates identity information across objects, thereby enhancing tracking robustness. Multi-Object Tracking (MOT) is a fundamental research topic in the field of computer vision, with broad application potential in unmanned aerial vehicle (UAV) videos. However, existing methods still face significant challenges in detection discriminability and identity association stability due to the small scale and weak appearance of objects under aerial viewpoints, as well as complex background interference. To address these issues, we propose an Edge-Graph Enhanced Network (EGEN) for UAV aerial MOT, aiming to improve the performance of small object detection (SOD) and tracking in complex scenes. The framework follows a one-step tracking paradigm and consists of three main components: object detection, embedding feature extraction, and data association. In the detection stage, we design an Edge-Guided Gaussian Enhancement Module (EGGEM), which models edge relationships between objects and backgrounds from a global perspective and selectively enhances Gaussian features guided by edge information, thereby strengthening key structural features of small objects while suppressing background interference. In the embedding feature extraction stage, we develop a Graph-Guided Embedding Enhancement Module (GGEEM), which explicitly represents re-identification (ReID) embeddings as a graph structure and jointly models nodes and their neighborhood relationships to fully capture inter-object associations and enhance embedding discriminability. In the data association stage, we introduce a hierarchical two-stage association strategy to match objects with different confidence levels separately, improving tracking stability and robustness. Extensive experiments on the VisDrone, UAVDT, and self-constructed WildDrone datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in both SOD and MOT, demonstrating strong generalization and practical applicability. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Object recognition (Computer vision)
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            NameFull: Xu, Yiming
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              Text: Mar2026
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