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Automatic detection and tracking of reappearing targets in forward-looking infrared imagery

机译:在向前展示红外图像中重新出现目标的自动检测和跟踪

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Target detection and tracking algorithms deal with the recognition of a variety of target images obtained from a multitude of sensor types, such as forward-looking infrared (FLIR), synthetic aperture radar and laser radar.1,2 Temporary disappearance and then reappearance of the target(s) in the field-of-view may be encountered during the tracking processes. To accommodate this problem, training based techniques have been developed using combination of two techniques; tuned basis functions (TBF) and correlation based template matching (TM) techniques. The TBFs are used to detect possible tentative target images. The detected candidate target images are then introduced into the second algorithm, called clutter rejection module, to determine the target reentering frame and location of the target. The performance of the proposed TBF-TM based reappeared target detection and tracking algorithm has been tested using real-world forward looking infrared video sequences
机译:目标检测和跟踪算法处理从多个传感器类型获得的各种目标图像,例如前瞻性红外线(FLIR),合成孔径雷达和激光雷达1.2临时消失,然后重新出现在跟踪过程中可能会遇到视野中的目标。为了适应这个问题,已经使用两种技术的组合开发了基于训练的技术;调整基本函数(TBF)和基于相关的模板匹配(TM)技术。 TBF用于检测可能的暂定目标图像。然后将检测到的候选目标图像引入到第二算法中,称为杂波抑制模块,以确定目标重新输入帧和目标的位置。使用真实的前瞻性红外视频序列测试了所提出的TBF-TM基于重新出现的目标检测和跟踪算法的性能

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