红外小目标检测与跟踪算法研究
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摘要
红外小目标的检测与跟踪是制导与预警等领域的一项关键技术。在尽可能远的距离上检测并跟踪到敌方目标,以争取在最有利的时机发动攻击,是决定现代战争胜负的重要因素。距离越远,目标成像面积越小,图像质量越差,对目标的检测与跟踪越困难。因此,研究小目标的检测与跟踪算法,对提高红外成像系统的作用距离,具有重要意义。
红外图像信杂比低、对比度差,目标尺寸小、形状和纹理等信息匮乏,相对于其他目标,红外小目标的检测与跟踪十分困难。机载前视红外图像背景复杂,目标与杂波交织在一起,形成大量虚假目标,进一步增加了检测与跟踪的难度。本文着重对机载前视红外探测系统远距离成像时复杂背景下的小目标图像预处理、小目标检测与跟踪问题进行研究。
1.红外小目标图像预处理算法研究
图像预处理的目的是抑制背景中的杂波,现有算法主要针对云杂波和海杂波进行滤除,对地面复杂背景下的杂波抑制效果较差。本文在分析机载前视红外图像特点的基础上,提出了一种基于多结构元素的形态重构开Top-Hat算子滤波算法,有效地去除了背景中的杂波;针对以上算法滤波效果仍然依赖于结构元素的形式及个数的问题,又提出了采用神经网络分类器自动获取标记图像的自适应滤波算法,避免了结构元素的选取问题,进一步提高了背景杂波的抑制能力;针对滤波处理后某些图像对比度较低的问题,提出了一种基于小波变换的目标能量增强算法,融合小波能量图像与小波变换低频图像,提高了图像的对比度,更利于对小目标的检测。
2.红外图像小目标检测算法研究
由于目标本身能量较弱,又受到背景杂波的影响,很难在单帧红外图像中准确地检测出小目标,因此常采用多帧检测算法。现有跟踪前检测(DBT)算法在图像信杂比较低时检测效果较差,检测前跟踪(TBD)算法计算量大,难以实时应用。针对上述问题,本文在图像预处理的基础上,将基于核密度估计的Mean-Shift跟踪引入到小目标检测算法中,提出了一种DBT类小目标检测算法。融合初始多帧图像,检测出候选目标,提高小目标的检测概率;采用Mean-Shift算法对候选目标进行跟踪,根据目标运动的连续性,通过跟踪轨迹确认真实小目标,剔除伪目标,降低虚警率;采用伪透视运动模型对传感器全局运动进行补偿,减少由于图像抖动而引起的真实目标漏检。综合以上技术,提高了对小目标的检测成功率,降低了虚警率。
3.红外图像小目标跟踪算法研究
在基于模型驱动的跟踪算法中,小目标无明显的形状和纹理信息可以利用,较难对其建立完善的数学模型,且常由于目标模板不能及时更新或过更新引起跟踪失败。本文提出一种基于多特征核密度估计的Mean-Shift跟踪算法。融合灰度和局部加权灰度信息熵特征,对目标模板与候选目标区域进行核密度估计,通过Mean-Shift算法最小化目标候选区域的核密度分布与模板的核密度分布之间的距离来实现跟踪;以Bhattacharyya系数为准则,对目标模板进行自动更新。此算法提高了对小目标跟踪的鲁棒性。
Infrared (IR) small target detection and tracking are key techniques in the fields of guidance, early warning and so on. In the modern wars, it is the vital factors, that detecting and tracking the enemy targets as far as possible in order to attack them at the most favorable time, which even decide the final results of the wars. The farther away from the detector, the smaller of the target size, the poorer of the image quality, and the more difficult to detect and track the targets. Therefore, the research on IR small target detection and tracking algorithms is of great significance to enhance the operating range of IR imaging system.
Compared to target detection and tracking in the other area, infrared small target detection and tracking are even more difficult due to several aspects, including the low signal-to-clutter ratio, low contrast of IR image, and the small size, lack of shape and texture information of the target. Especially, the background in airborne Forward-looking infrared (FLIR) image is complex, the targets are intertwined with the clutter and there are numbers of false targets that disturb the detection and tracking. The works presented in this dissertation focus on image pre-processing, small target detection and tracking algorithm in the long-range imaging airborne FLIR image sequence under complex background.
1. Research on infrared small target image pre-processing algorithm
The image pre-processing aims at suppression of the clutter in the background. The existing algorithms are mainly developed to restrain the cloud clutter and sea clutter and appear less effective in the clutter suppression under complex ground backgrounds. On the basis of analyzing the characteristics of the airborne FLIR images, A Multiple Structuring Elements Opening-by-Reconstruction Top-Hat Operator is proposed, which can effectively remove the clutter from the background. Then, according to the filtering result relying on the structuring elements, this study goes on to mark the images automatically using neural network classifier, thus avoiding the problem in the selection of structural elements, implementing the adaptive filter and further enhancing the ability to suppress the background clutter. In addition, considering the existence of certain lower contrast image after the process of filtering, a Target Energy Enhancement Algorithm Based on Wavelet Transform is given, which combines the wavelet energy image and the wavelet transform scaled image, to improve the image contrast and thus contribute to the detection of small targets.
2. Research on infrared small targets detection algorithm
Due to the less energy of small targets and the impact of background clutter, it is difficult to accurately detect the small targets in a single frame of FLIR images. Therefore, Multi-frame detection algorithm is typically used. However, the existing Detect-before-Track (DBT) algorithm appears less effective dealing with the images at a relatively low SCR, and the Track-before-Detect (TBD) algorithm is not suitable for real-time applications due to its large amounts of computing. According to these questions, based on image pre-processing, the kernel based Mean-Shift tracking is introduced into small target detection algorithm, a DBT algorithm is proposed: detecting the target candidates by integrating the initial multi-frame images to enhance the probability of detecting; according to the continuity of target, tracking the target candidates using Mean-Shift algorithm to remove the fake targets and reduce the false alarm; compensating the global motion of the sensor using pseudo-perspective motion model to reduce the missing of detection caused by the images shaking. Taking all these technologies above, we finally achieve the fast and accurate detection of small targets.
3. Research on infrared small targets tracking algorithm
In the Model-driven Tracking algorithm, little information on the shape and texture of the small target can be used, it is difficult to set up a consummate mathematical model, and often causes the tracking failure due to delay-update or over-update of the template. In this study, a Mean-Shift Tracking algorithm based on multi-characteristics kernel density estimation is proposed. The study goes on to integrate gray-scale and the local weighted intensity entropy features to estimate the kernel density of the template and the region of target candidate, implements the tracking mission by minimizing distance between the kernel density distribution of the target candidate region and that of the template, and then automatically updates the target template according to the Bhattacharyya coefficient. This algorithm achieves the robust tracking of small targets.
红外图像信杂比低、对比度差,目标尺寸小、形状和纹理等信息匮乏,相对于其他目标,红外小目标的检测与跟踪十分困难。机载前视红外图像背景复杂,目标与杂波交织在一起,形成大量虚假目标,进一步增加了检测与跟踪的难度。本文着重对机载前视红外探测系统远距离成像时复杂背景下的小目标图像预处理、小目标检测与跟踪问题进行研究。
1.红外小目标图像预处理算法研究
图像预处理的目的是抑制背景中的杂波,现有算法主要针对云杂波和海杂波进行滤除,对地面复杂背景下的杂波抑制效果较差。本文在分析机载前视红外图像特点的基础上,提出了一种基于多结构元素的形态重构开Top-Hat算子滤波算法,有效地去除了背景中的杂波;针对以上算法滤波效果仍然依赖于结构元素的形式及个数的问题,又提出了采用神经网络分类器自动获取标记图像的自适应滤波算法,避免了结构元素的选取问题,进一步提高了背景杂波的抑制能力;针对滤波处理后某些图像对比度较低的问题,提出了一种基于小波变换的目标能量增强算法,融合小波能量图像与小波变换低频图像,提高了图像的对比度,更利于对小目标的检测。
2.红外图像小目标检测算法研究
由于目标本身能量较弱,又受到背景杂波的影响,很难在单帧红外图像中准确地检测出小目标,因此常采用多帧检测算法。现有跟踪前检测(DBT)算法在图像信杂比较低时检测效果较差,检测前跟踪(TBD)算法计算量大,难以实时应用。针对上述问题,本文在图像预处理的基础上,将基于核密度估计的Mean-Shift跟踪引入到小目标检测算法中,提出了一种DBT类小目标检测算法。融合初始多帧图像,检测出候选目标,提高小目标的检测概率;采用Mean-Shift算法对候选目标进行跟踪,根据目标运动的连续性,通过跟踪轨迹确认真实小目标,剔除伪目标,降低虚警率;采用伪透视运动模型对传感器全局运动进行补偿,减少由于图像抖动而引起的真实目标漏检。综合以上技术,提高了对小目标的检测成功率,降低了虚警率。
3.红外图像小目标跟踪算法研究
在基于模型驱动的跟踪算法中,小目标无明显的形状和纹理信息可以利用,较难对其建立完善的数学模型,且常由于目标模板不能及时更新或过更新引起跟踪失败。本文提出一种基于多特征核密度估计的Mean-Shift跟踪算法。融合灰度和局部加权灰度信息熵特征,对目标模板与候选目标区域进行核密度估计,通过Mean-Shift算法最小化目标候选区域的核密度分布与模板的核密度分布之间的距离来实现跟踪;以Bhattacharyya系数为准则,对目标模板进行自动更新。此算法提高了对小目标跟踪的鲁棒性。
Infrared (IR) small target detection and tracking are key techniques in the fields of guidance, early warning and so on. In the modern wars, it is the vital factors, that detecting and tracking the enemy targets as far as possible in order to attack them at the most favorable time, which even decide the final results of the wars. The farther away from the detector, the smaller of the target size, the poorer of the image quality, and the more difficult to detect and track the targets. Therefore, the research on IR small target detection and tracking algorithms is of great significance to enhance the operating range of IR imaging system.
Compared to target detection and tracking in the other area, infrared small target detection and tracking are even more difficult due to several aspects, including the low signal-to-clutter ratio, low contrast of IR image, and the small size, lack of shape and texture information of the target. Especially, the background in airborne Forward-looking infrared (FLIR) image is complex, the targets are intertwined with the clutter and there are numbers of false targets that disturb the detection and tracking. The works presented in this dissertation focus on image pre-processing, small target detection and tracking algorithm in the long-range imaging airborne FLIR image sequence under complex background.
1. Research on infrared small target image pre-processing algorithm
The image pre-processing aims at suppression of the clutter in the background. The existing algorithms are mainly developed to restrain the cloud clutter and sea clutter and appear less effective in the clutter suppression under complex ground backgrounds. On the basis of analyzing the characteristics of the airborne FLIR images, A Multiple Structuring Elements Opening-by-Reconstruction Top-Hat Operator is proposed, which can effectively remove the clutter from the background. Then, according to the filtering result relying on the structuring elements, this study goes on to mark the images automatically using neural network classifier, thus avoiding the problem in the selection of structural elements, implementing the adaptive filter and further enhancing the ability to suppress the background clutter. In addition, considering the existence of certain lower contrast image after the process of filtering, a Target Energy Enhancement Algorithm Based on Wavelet Transform is given, which combines the wavelet energy image and the wavelet transform scaled image, to improve the image contrast and thus contribute to the detection of small targets.
2. Research on infrared small targets detection algorithm
Due to the less energy of small targets and the impact of background clutter, it is difficult to accurately detect the small targets in a single frame of FLIR images. Therefore, Multi-frame detection algorithm is typically used. However, the existing Detect-before-Track (DBT) algorithm appears less effective dealing with the images at a relatively low SCR, and the Track-before-Detect (TBD) algorithm is not suitable for real-time applications due to its large amounts of computing. According to these questions, based on image pre-processing, the kernel based Mean-Shift tracking is introduced into small target detection algorithm, a DBT algorithm is proposed: detecting the target candidates by integrating the initial multi-frame images to enhance the probability of detecting; according to the continuity of target, tracking the target candidates using Mean-Shift algorithm to remove the fake targets and reduce the false alarm; compensating the global motion of the sensor using pseudo-perspective motion model to reduce the missing of detection caused by the images shaking. Taking all these technologies above, we finally achieve the fast and accurate detection of small targets.
3. Research on infrared small targets tracking algorithm
In the Model-driven Tracking algorithm, little information on the shape and texture of the small target can be used, it is difficult to set up a consummate mathematical model, and often causes the tracking failure due to delay-update or over-update of the template. In this study, a Mean-Shift Tracking algorithm based on multi-characteristics kernel density estimation is proposed. The study goes on to integrate gray-scale and the local weighted intensity entropy features to estimate the kernel density of the template and the region of target candidate, implements the tracking mission by minimizing distance between the kernel density distribution of the target candidate region and that of the template, and then automatically updates the target template according to the Bhattacharyya coefficient. This algorithm achieves the robust tracking of small targets.
引文
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