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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.05229 |
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| _version_ | 1866909677687668736 |
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| author | Kostiv, Markiyan Adamovskyi, Anatolii Cherniavskyi, Yevhen Varenyk, Mykyta Viniavskyi, Ostap Krashenyi, Igor Dobosevych, Oles |
| author_facet | Kostiv, Markiyan Adamovskyi, Anatolii Cherniavskyi, Yevhen Varenyk, Mykyta Viniavskyi, Ostap Krashenyi, Igor Dobosevych, Oles |
| contents | Multi-object tracking (MOT) aims to maintain consistent identities of objects across video frames. Associating objects in low-frame-rate videos captured by moving unmanned aerial vehicles (UAVs) in actual combat scenarios is complex due to rapid changes in object appearance and position within the frame. The task becomes even more challenging due to image degradation caused by cloud video streaming and compression algorithms. We present how instance association learning from single-frame annotations can overcome these challenges. We show that global features of the scene provide crucial context for low-FPS instance association, allowing our solution to be robust to distractors and gaps in detections. We also demonstrate that such a tracking approach maintains high association quality even when reducing the input image resolution and latent representation size for faster inference. Finally, we present a benchmark dataset of annotated military vehicles collected from publicly available data sources. This paper was initially presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST)Scientific and Technical Committee, IST-209-RSY - the ICMCIS, held in Oeiras, Portugal, 13-14 May 2025. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Real-Time Tracking of Military Vehicles in Low-FPS UAV Footage Kostiv, Markiyan Adamovskyi, Anatolii Cherniavskyi, Yevhen Varenyk, Mykyta Viniavskyi, Ostap Krashenyi, Igor Dobosevych, Oles Computer Vision and Pattern Recognition Multi-object tracking (MOT) aims to maintain consistent identities of objects across video frames. Associating objects in low-frame-rate videos captured by moving unmanned aerial vehicles (UAVs) in actual combat scenarios is complex due to rapid changes in object appearance and position within the frame. The task becomes even more challenging due to image degradation caused by cloud video streaming and compression algorithms. We present how instance association learning from single-frame annotations can overcome these challenges. We show that global features of the scene provide crucial context for low-FPS instance association, allowing our solution to be robust to distractors and gaps in detections. We also demonstrate that such a tracking approach maintains high association quality even when reducing the input image resolution and latent representation size for faster inference. Finally, we present a benchmark dataset of annotated military vehicles collected from publicly available data sources. This paper was initially presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST)Scientific and Technical Committee, IST-209-RSY - the ICMCIS, held in Oeiras, Portugal, 13-14 May 2025. |
| title | Self-Supervised Real-Time Tracking of Military Vehicles in Low-FPS UAV Footage |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.05229 |