Object detection and tracking is always a challenging issue in UAV (unmanned aerial vehicle) application. Especially, in the scene of UAV-ASV (autonomous surface vehicle) cooperative system, UAV vision based target tracking performance has been suffering from the target rotation and fast motion. Aiming at optimizing the related UAV vision tracking performance, a SDSST tracker(strong discriminative scale space tracking) with automatic initialization and self-adjusting is developed in this paper. Firstly, in the step of initialization, combining the advantages of both fast optimization of BLS (Broad Learning System) and efficient image processing of CNN (convolutional neural networks), a novel DBLS (Deep Broad Learning System) is posed for the target detection. Meanwhile, a Q-learning based DBLS architecture searching is further introduced. Then, in terms of width/height ratio self-adjusting, this article proposed a novel filter state supervisor that helps to find the abnormal estimated state caused by rotation in target scale estimation. Basically, this so called filter state supervisor could take RSV (Rolling Standard Value) as input feature and give out the filter state. Finally, the abnormal filter state would be adjusted by an appropriate alternative by searching in the proposed rotation angles memory, so that an optimized self-adjusting could be realized. Meanwhile, extensive experiments are performed on data set of USV center in Qiandao Lake, yielding a competitive result compared with five other prevalent trackers. Note to Practitioners—This paper was motivated by the problem of target lost caused by sudden change of target motion in the UAV-ASV vision tracking. Usually, the rotation motion of ASV is a major operation when ASV carries out a maritime assignment. However, the poor tracking state supervision and inflexible scale updating method as well as manual initialization in the existing approaches lead to inappropriate scale and target lost when the target undergoes rotational motion. Therefor, this paper proposes a strong vision tracker (SDSST) to enhance the original DSST in the three aspects: automatic initialization, filter state supervisor, and self-adjusting. This can allow the tracker to initialize without human interference, also bad tracker state can be informed by filter state supervisor. When bad state happens that the target scale in the tracker will be updated flexibly based on proposed rotation angles memory. Finally, The proposed method is implemented on the real filed UAV vision data collected by UAV-ASV system in the Qiandao Lake. The results show that SDSST achieves competitive result compared to 7 other prevalent trackers.

中文翻译：

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基于深度广博学习系统和相关滤波器的强大无人机视觉跟踪器


目标检测和跟踪始终是无人机应用中的一个具有挑战性的问题。特别是在无人机-ASV（自主水面车辆）协作系统场景中，无人机基于视觉的目标跟踪性能一直受到目标旋转和快速运动的影响。针对优化无人机视觉跟踪相关性能，本文研制了一种具有自动初始化和自调节功能的SDSST跟踪器（强判别尺度空间跟踪）。首先，在初始化步骤中，结合BLS（广泛学习系统）快速优化和CNN（卷积神经网络）高效图像处理的优点，提出了一种新颖的DBLS（深度广泛学习系统）用于目标检测。同时，进一步介绍了基于Q-learning的DBLS架构搜索。然后，在宽高比自调整方面，本文提出了一种新颖的滤波器状态监控器，有助于发现目标尺度估计中因旋转而导致的异常估计状态。基本上，这个所谓的过滤器状态管理器可以将 RSV（滚动标准值）作为输入特征并给出过滤器状态。最后，通过在所提出的旋转角度存储器中搜索，通过适当的替代方案来调整异常的滤波器状态，从而可以实现优化的自调整。同时，在千岛湖USV中心的数据集上进行了大量的实验，与其他五个流行的跟踪器相比，取得了有竞争力的结果。从业者须知——本文的出发点是针对无人机-ASV视觉跟踪中目标运动突变导致的目标丢失问题。通常，ASV的旋转运动是ASV执行海上任务时的主要​​操作。 然而，现有方法中较差的跟踪状态监督和不灵活的尺度更新方法以及手动初始化导致目标进行旋转运动时尺度不合适和目标丢失。因此，本文提出了一种强视觉跟踪器（SDSST），以在自动初始化、滤波器状态监控和自调整三个方面对原始DSST进行增强。这可以允许跟踪器在没有人为干扰的情况下进行初始化，并且过滤器状态管理器也可以通知不良的跟踪器状态。当发生不良状态时，跟踪器中的目标比例将根据建议的旋转角度记忆灵活更新。最后，在千岛湖无人机ASV系统采集的实景无人机视觉数据上实现了该方法。结果表明，与其他 7 种流行的跟踪器相比，SDSST 取得了有竞争力的结果。