主讲人:FLIR图像中的超快速模式识别和跟踪

M. Alam
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引用次数: 0

摘要

由于低分辨率、低信噪比、目标的三维方向不同、全局运动的影响以及与相似物体的近距离接近等因素,前视红外(FLIR)图像中的模式识别和跟踪是一个具有挑战性的问题。在本次主题演讲中,我们将回顾畸变不变模式识别的最新趋势和进展,然后开发一种用于FLIR图像中单/多目标检测和跟踪的新型数据融合算法。每种检测/跟踪算法都利用给定FLIR序列的物体和图像帧的各种属性。数据融合算法利用上述两种或两种以上算法的互补特征,显著提高了检测/跟踪精度。使用真实的前红外图像序列的测试结果证实了该技术的有效性。
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Keynote speakers: Ultrafast pattern recognition and tracking in FLIR imagery
Pattern recognition and tracking in forward looking infrared (FLIR) imagery is a challenging problem due to various factors such as low resolution, low signal-to-noise ratio, different 3D orientations of the targets, effects of global motion, and close proximity with similar objects. In this keynote, we will review the recent trends and advancements in distortion-invariant pattern recognition followed by the development of a novel data fusion algorithm for single/multiple object detection and tracking in FLIR imagery. Each detection/tracking algorithm utilizes various properties of objects and image frames of a given FLIR sequence. The data fusion algorithm employs complementary features of two or more of the aforementioned algorithms to achieve significantly better detection/tracking accuracy. Test results using real life FLIR image sequences confirm the effectiveness of the proposed technique.
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