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Research on Aerial Object Tracking Algorithm Based on Multi-tracker Relay

机译:基于多跟踪器中继的空中目标跟踪算法研究

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Aerial object tracking technology is widely used in unmanned aerial vehicle, air traffic control and drone counters.The tracking algorithm using only a single feature is difficult to adapt to the complex tracking environment. How toachieve robust tracking of aerial objects under different interference conditions is the main problem studied in this paper.To solve this problem, this paper proposed a multi-feature fusion tracking algorithm based on confidence evaluation. Atthe same time, in order to solve the problem of large variation of aerial object scale, a parallel scale estimation strategybased on the Discriminative Scale Space Tracking algorithm is proposed in this paper. This method estimates the optimalwidth and height of the object respectively, which can estimate the scale variation of aerial object more stably. For theocclusion problem, an occlusion-aware part-based model is proposed in this paper. The part-based local model can dealwith the occlusion problem effectively, while the global model is more suitable for dealing with the fast motion andmotion blur of the object. Therefore, a tracking method based on multi-tracker relay is proposed in this paper. In thismethod, the tracking state is judged according to the confidence of the model. In the normal tracking state, the globalmodel based on multi-feature fusion is used, and when occlusion interference exists, the tracking model is replaced bythe parts-based local model. In this way, the algorithm can effectively deal with various tracking situations.
机译:空中物体跟踪技术被广泛应用于无人机,空中交通管制和无人机柜台。 仅使用单个特征的跟踪算法难以适应复杂的跟踪环境。如何 本文研究的主要问题是在不同的干扰条件下实现对空中物体的鲁棒跟踪。 为了解决这个问题,本文提出了一种基于置信度评估的多特征融合跟踪算法。在 同时,为了解决空中物体尺度变化较大的问题,提出了一种并行尺度估计策略 本文提出了一种基于判别尺度空间跟踪的算法。该方法估计最佳 物体的宽度和高度,可以更稳定地估算空中物体的尺度变化。为了 遮挡问题,本文提出了一种基于遮挡感知的零件模型。基于零件的本地模型可以处理 有效地解决了遮挡问题,而全局模型更适合于处理快速运动和 对象的运动模糊。因此,本文提出了一种基于多跟踪器中继的跟踪方法。在这个 方法,根据模型的置信度判断跟踪状态。在正常跟踪状态下, 使用基于多特征融合的模型,当存在遮挡干扰时,将跟踪模型替换为 基于零件的局部模型。这样,该算法可以有效地处理各种跟踪情况。

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