Research On Infrared Dim And Small Target Detection And Tracking Technology

With the development of infrared imaging technology and the need of modern warfare, detectionand tracking technology of dim and small target in infrared image has become mainstream directionof infrared precision guidance technology in modern world. The quality of detection and trackingperformance of dim and small target directly determines the effective distance of guidance system andthe complexity of the device, and it plays a pivotal role in the infrared imaging guidance system. Inthis paper, three main technical aspects of dim and small target detection and tracking including imagepre-processing, object detection and object tracking are studied.First of all, the commonly used image pre-processing methods are analyzed from the timedomain filtering, spatial domain filtering, and frequency domain filtering in this paper. Visualattention mechanism is introduced on this basis, and a new image preprocessing algorithm based onvisual significance characteristics is also proposed in our paper. The algorithm rapidly search theentire input image to obtain a set of interested candidate targets by analysis the grayscale significanceof the objects in the image. Experimental results show that the new algorithm can effectively inhibitthe infrared image background. The new algorithm which is different from the majority of spatialdomain algorithm does not require background predict, and does not depend on prior knowledge,therefore it applies more broadly.Secondly, on account of low probability of detection, poor robustness, poor real-time problemsof the traditional single-frame detection algorithm, a detection algorithm based on multi-directionalcomposite window structure combined with the preprocessing framework of the last chapter isproposed. The gray level distribution of infrared image is analyzed in this algorithm. To detect thetarget by building window structure to compare pixels inside the windows through multi-directionalgrayscale mean value. Without binary segmentation, this algorithm avoids the detection resultsexcessively depend on the threshold selecting.Finally，to overcome the disadvantages of traditional Mean-shift algorithm such as the instabilityof target description and the low accuracy of tracing, an improved Mean-shift algorithm based onfusing multiple features is proposed. The hierarchical tracking method is used in this algorithm. Firstimproving the Mean-shift algorithm, and then combining the Harris feature matching algorithm withthe improved method. After the Mean-shift algorithm obtaining the position of the target in the currentinfrared images, we use the Harris feature matching method to correct the position coordinates, thus ensuring the tracking accuracy. The improved Mean-shift algorithm adopts several targetcharacteristics during target description, and considers the real-time updating problem of targettemplate in order to make the tracking strong robustness. Experimental results show that robust objectdescription, with the accurate correction to the tracking results of the feature matching, can effectivelyeliminate the deviation errors generated in the process of tracking, thus improving the accuracy oftracking and ensuring the accurate tracking of the target.