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Aircraft tracking based on fully conventional network and Kalman filter

机译：基于完全常规网络和卡尔曼滤波器的飞机跟踪

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Aircraft tracking is a significant technology for military reconnaissance, but there is no efficient algorithm to solve this particular problem. Recently, research based on deep learning for object tracking has developed rapidly, and the performance is greatly improved compared to the traditional methods, so the authors refer to relevant work and make an improvement on the previous research to improve the performance on aircraft tracking. They first learn the idea from region-based fully convolutional networks to perform detection on each frame of video. To avoid the target drift due to the failure of object detection on a certain frame, then they employ Kalman filter (KF) and extended KF together to predict the moving trajectory of the target. Beyond that, this method can confine the valid range based on the size of a target object, which increases the speed of detection. This approach can also correct the bounding box on adjacent frames. The steps are not complicated but have an excellent performance. Through the experiment, it is clear that the proposed method is reasonable and more precise.
机译：飞机跟踪是军事侦察的一项重要技术，但是没有有效的算法来解决这一特殊问题。近年来，基于深度学习的目标跟踪研究发展迅速，与传统方法相比，其性能有了很大提高，因此作者参考了相关工作并对以前的研究进行了改进，以提高飞机跟踪的性能。他们首先从基于区域的全卷积网络中学习了这种思想，以便对视频的每一帧进行检测。为了避免由于物体在特定帧上检测失败而导致的目标漂移，他们将卡尔曼滤波器（KF）和扩展的KF一起使用来预测目标的运动轨迹。除此之外，该方法可以根据目标物体的大小限制有效范围，从而提高了检测速度。这种方法还可以校正相邻帧上的边界框。这些步骤并不复杂，但是具有出色的性能。通过实验可以明显看出，该方法是合理，准确的。

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