RAMOTS：基于深度学习与大数据技术的航空多目标跟踪实时系统
RAMOTS: A Real-Time System for Aerial Multi-Object Tracking based on Deep Learning and Big Data Technology
AI总结：
本文提出RAMOTS实时航空多目标跟踪系统，融合Apache Kafka/Spark与YOLOv8/YOLOv10、BYTETRACK/BoTSORT，在Visdrone2019-MOT上取得HOTA 48.14、MOTA 43.51，并以28 FPS实时运行。
摘要：
基于无人机视频的多目标跟踪（MOT）因视角变化、低分辨率以及小目标的存在而具有挑战性。虽然其他针对航空视频的MOT研究主要通过开发复杂算法来关注学术层面，但缺乏对这些系统实际应用层面的关注。在本文中，我们提出了一种新颖的实时MOT框架，该框架集成Apache Kafka和Apache Spark以实现高效且容错的视频流处理，并结合最先进的深度学习模型YOLOv8/YOLOv10和BYTETRACK/BoTSORT来实现准确的目标检测与跟踪。我们的工作强调了不仅先进算法的重要性，还强调了将这些方法与可扩展的分布式系统相集成的重要性。通过利用这些技术，我们的系统在Visdrone2019-MOT测试集上取得了48.14的HOTA和43.51的MOTA，同时在单个GPU上保持了28 FPS的实时处理速度。我们的工作展示大数据技术和深度学习在应对无人机应用中MOT挑战方面的潜力。
英文摘要：
Multi-object tracking (MOT) in UAV-based video is challenging due to variations in viewpoint, low resolution, and the presence of small objects. While other research on MOT dedicated to aerial videos primarily focuses on the academic aspect by developing sophisticated algorithms, there is a lack of attention to the practical aspect of these systems. In this paper, we propose a novel real-time MOT framework that integrates Apache Kafka and Apache Spark for efficient and fault-tolerant video stream processing, along with state-of-the-art deep learning models YOLOv8/YOLOv10 and BYTETRACK/BoTSORT for accurate object detection and tracking. Our work highlights the importance of not only the advanced algorithms but also the integration of these methods with scalable and distributed systems. By leveraging these technologies, our system achieves a HOTA of 48.14 and a MOTA of 43.51 on the Visdrone2019-MOT test set while maintaining a real-time processing speed of 28 FPS on a single GPU. Our work demonstrates the potential of big data technologies and deep learning for addressing the challenges of MOT in UAV applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
