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首页> 外文期刊>Indian Journal of Science and Technology >Maritime Vessels Real-time Tracking-by-detection in UAV Videos
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Maritime Vessels Real-time Tracking-by-detection in UAV Videos

机译:无人机视频中按检测实时跟踪

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摘要

Background/Objectives: This paper presents a fast real-time multi-object tracking algorithm adopted for maritime vessels tracking in UAV videos. Methods/Statistical Analysis: This research applies extended multi-object tracking-bydetection framework in highly dynamic UAV-captured maritime environment. Propagation is performed using particle filter whose particle weights are updated using modified observation model that incorporates a term based upon trackers affinity. The latter is also used for trackers grouping, that handles detector inaccuracies, and preventing identity switches by means of special propagation mode that is enabled when targets approach each other and start to overlap. Findings: Our research has shown that state-of-art multi-tracking algorithm is applicable to maritime vessels real-time tracking in UAV videos, provided the use of weak and fast online classifiers, which weakness is compensated by algorithm features based on trackers affinity matrix. Improvements/Applications: This paper presents an approach to maritime vessels tracking that allows to use fast, but less accurate, simple detectors and classifiers, enabling real-time processing on board of small-sized UAVs, while keeping decent precision and accuracy.
机译:背景/目的:本文提出了一种用于无人机视频中海上船只跟踪的快速实时多目标跟踪算法。方法/统计分析:本研究在高动态无人机捕获的海洋环境中应用了扩展的多目标跟踪检测框架。使用粒子过滤器执行传播,该粒子过滤器的粒子权重使用修改后的观察模型进行更新,该模型结合了基于跟踪器亲和力的术语。后者还用于跟踪器分组,处理检测器的不准确性,并通过特殊的传播模式防止身份切换,当目标彼此靠近并开始重叠时启用该特殊传播模式。发现:我们的研究表明,只要使用了弱而快速的在线分类器,先进的多重跟踪算法就可以应用于无人机视频中的海上船舶实时跟踪,该弱点可以通过基于跟踪器相似性的算法功能来弥补矩阵。改进/应用:本文介绍了一种海上船只跟踪方法,该方法允许使用快速但准确度较低的简单检测器和分类器,从而可以在小型无人机上进行实时处理,同时保持良好的精度和准确性。

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