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

机译:基于多跟踪继电器的空中物体跟踪算法研究

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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很难适应复杂的跟踪环境。如何在不同的干扰条件下实现空中物体的稳健跟踪是本文研究的主要问题。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,为了解决空中物体量表的大变化问题,并行规模估计策略基于本文提出了基于鉴别规模的空间跟踪算法。该方法估计最佳物体的宽度和高度,可以更稳定地估计空中物体的比例变化。为了闭塞问题,本文提出了一种遮挡感知部分的模型。基于零件的本地模型可以处理随着遮挡问题有效,而全球模型更适合处理快速运动和物体的运动模糊。因此,本文提出了一种基于多跟踪继电器的跟踪方法。在这方面方法,根据模型的置信判断跟踪状态。在正常的跟踪状态下,全局使用基于多重特征融合的模型,并且当遮挡干扰存在时,跟踪模型被替换为基于零件的本地模型。以这种方式,算法可以有效地处理各种跟踪情况。

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