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Improved motion information-based infrared dim target tracking algorithms

机译：改进的基于运动信息的红外昏暗目标跟踪算法

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摘要

Accurate and fast tracking of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. However, under complex backgrounds, such as clutter, varying illumination, and occlusion, the traditional tracking method often converges to a local maximum and loses the real infrared target. To cope with these problems, three improved tracking algorithm based on motion information are proposed in this paper, namely improved mean shift algorithm, improved Optical flow method and improved Particle Filter method. The basic principles and the implementing procedure of these modified algorithms for target tracking are described. Using these algorithms, the experiments on some real-life IR and color images are performed. The whole algorithm implementing processes and results are analyzed, and those algorithms for tracking targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying tracking effectiveness and robustness. Meanwhile, it has high tracking efficiency and can be used for real-time tracking.
机译：准确，快速地跟踪红外暗淡目标对于红外精确制导，预警，视频监视等具有非常重要的意义。但是，在复杂的背景下，例如混乱，照明变化和遮挡，传统的跟踪方法通常会收敛达到局部最大值，并失去实际的红外目标。针对这些问题，本文提出了三种基于运动信息的改进跟踪算法，即改进的均值漂移算法，改进的光流法和改进的粒子滤波法。描述了这些改进的目标跟踪算法的基本原理和实现过程。使用这些算法，对一些真实的IR和彩色图像进行了实验。分析了整个算法的实现过程和结果，从主观和客观两个方面对目标跟踪算法进行了评价。结果证明，该方法具有令人满意的跟踪效果和鲁棒性。同时，跟踪效率高，可用于实时跟踪。

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