用大数据框架+深度学习实现实时无人机多目标跟踪
RAMOTS: A Real-Time System for Aerial Multi-Object Tracking based on Deep Learning and Big Data Technology
- 融合Kafka与Spark处理视频流，支持分布式实时计算
- 在Visdrone2019-MOT上达48.14的HOTA和28帧/秒速度
- 适合需要高可靠实时追踪的无人机应用开发
基于无人机的视频多目标跟踪因视角变化、分辨率低及小目标存在而极具挑战。现有研究多聚焦于算法创新，忽视系统实用性。本文提出一种新型实时多目标跟踪框架，结合Apache Kafka与Apache Spark实现高效容错的视频流处理，并集成YOLOv8/YOLOv10与BYTETRACK/BoTSORT等先进深度学习模型，实现精准检测与跟踪。该系统不仅具备先进算法，更强调其与可扩展分布式系统的融合。在Visdrone2019-MOT测试集上，系统达到48.14的HOTA和43.51的MOTA，且在单张GPU上保持28 FPS的实时处理速度。结果表明，大数据技术与深度学习的结合可有效应对无人机场景下的多目标跟踪难题。
原文摘要 · Abstract (English)
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.
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