CloudTrack: Scalable UAV Tracking with Cloud Semantics
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Abstract (arXiv)
Nowadays, unmanned aerial vehicles (UAVs) are commonly used in search and rescue scenarios to gather information in the search area. The automatic identification of the person searched for in aerial footage could increase the autonomy of such systems, reduce the search time, and thus increase the missed person's chances of survival. In this paper, we present a novel approach to perform semantically conditioned open vocabulary object tracking that is specifically designed to cope with the limitations of UAV hardware. Our approach has several advantages. It can run with verbal descriptions of the missing person, e.g., the color of the shirt, it does not require dedicated training to execute the mission and can efficiently track a potentially moving person. Our experimental results demonstrate the versatility and efficacy of our approach.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 24 Sep 2024.
Builds on
15 of 28 references.
References · 15
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
- 2024PaliGemma: A versatile 3B VLM for transfercited by 679
- 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 428
- 2023Tracking Anything with Decoupled Video Segmentationcited by 197
- 2018Distractor-aware Siamese Networks for Visual Object Trackingcited by 187
- 2016Learning to Track at 100 FPS with Deep Regression Networkscited by 89
- 2021LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Searchcited by 82
- 2023General Object Foundation Model for Images and Videos at Scalecited by 71
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.