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Inter-frame Correlation Based on Moving Vehicle Target Detection in Infrared Image Sequences

机译:基于运动车辆目标检测的红外图像序列帧间相关

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Moving vehicle targets detection in infrared image sequences is playing a more and more important role in infrared search and track systems. This paper presents a novel method based on inter-frame correlation to detect moving vehicle target in infrared image sequences reliably. Firstly, for a single frame, the image respectively is sharpened and enhanced after image denoising, and then generating the preprocessed image. Secondly, the vehicle targets in infrared image sequences are detected by a saliency based target detection algorithm. For consecutive frames, features of motion between real vehicle targets and false ones are different, then the inter-frame correlation is operated to suppress the false alarm, making the vehicle targets detection more accurate. The experiments on the image sequences demonstrate that the proposed method has ensure detection effectiveness and robustness for vehicle detection in infrared image sequences under complex backgrounds, and it also improve the reliability.
机译:红外图像序列中运动车辆目标的检测在红外搜索和跟踪系统中扮演着越来越重要的角色。提出了一种基于帧间相关性的红外图像序列中移动车辆目标的可靠检测方法。首先,对于单个帧,在图像去噪后分别对图像进行锐化和增强,然后生成预处理图像。其次,通过基于显着性的目标检测算法来检测红外图像序列中的车辆目标。对于连续的帧,真实车辆目标与错误目标之间的运动特征是不同的,然后操作帧间相关性以抑制错误警报,从而使车辆目标检测更加准确。在图像序列上的实验表明,该方法在复杂背景下,具有一定的检测效果和鲁棒性,可用于车辆图像的红外检测。

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