Study On Infrared Target Detection And Tracking Under Complex Backgrounds

Infrared image processing is a key technique in the application field of early warning, safety monitoring and control, automatic search and tracking, missile guidance etc. Many researchers commit themselves to the study on this technique. The modern battles are intense confrontation with advanced technologies. For gaining dominance on the field of battle, the arm systems have to detect and track targets from far distance in order to save more time for dealing with the complexion of the war. Under this condition, infrared targets have small size and poor contrast without texture and configuration features. Therefore the detection and tracking of these targets are very difficult. Moreover, the background of infrared targets is complex with much clutter and noise, which makes it more difficult to dectect and track targets.Some methods for decting and tracking infrared targets have been proposed so far. Classical methods are based on high-pass or low-pass filters and order-statistical filters. The tow-dimensional least mean square filters is another method based on filters. With the development of pattern recognition, detecting targets with this theory is becoming a new research field. For tracking targets, the tracking methods designed specially for infrared targets are not proposed. So we have to use the methods designed for color images, such as mean shift and particle filter. Although they have good tracking precision for color images, their tracking effects are not satisfying for infrared images whose features are not distinct.This dissertation focuses on finding novel detection and tracking algorithms for infrared targets. One digital image with targets can be treated as a datum set consisting of target data and background data. Detecting targets is to find target data from the datum set. So the target detection problem is transformed into a tow-pattern recognition problem and any pattern recognition method can be used to detect targets. In this dissertation, we mainly use sub-space methods to detect targets and propose the concepts, Eigentargets and Fishertarget. Moreover, another sub-space method, FKT, also is researched on its detection performance. The linear sub-space methods are appropriate for data that are generated by Gaussian distribution, or data that are best described by second-order correlations without taking higher-order statistics into account. For capturing higher-order dependencies in the data, the nonlinear sub-space detection methods are proposed. The features of original infrared images are poor and faint. For improving the tracking precision, we track infrared targets in multiple feature images. They are synthesized by Gabor feature images, entropy feature images and original infrared images. Because the distinction between targets and background is improved, the mean shift can gain better tracking performance.Specifically, the main contributions of this dissertation are as follows:1. For supervised learning methods, training samples are very important. The appropriate training samples can correctly reflect the performance of recognition methods. The Gaussian intensity model (GIM) is a commonly used model to represent infrared point targets and applied to create training samples, simulated images, of infrared point targets. However, it may produce sample outliers which will reduce the recognition rate. We modify the GIM to avoid producing outliers. This is a foundation of exploring the detection methods, based on pattern recognition theory, for infrared point targets.2. Linear sub-space methods have been successfully applied to the field of face recognition. However, no one systematicly proposes the detection methods for infrared point targets which are based on linear sub-space methods. The concepts of Eigentargets and Fishertarget are given in this dissertation. The difference between projecting coefficients and the projecting coefficients of training samples of targets can indicate the resembling degree of the image and targets. So we can find targets from images. Moreover, we give a detection function to produce detection images which makes the performance comparison of detection methods be more convenient. To further improve detection performance, the linear sub-space methods are expanded into kernel methods (nonlinear sub-space methods) to detect infrared point targets. The linear sub-space methods can only capture the second-order statistics which is not sufficient for representing target images and background images. However, kernel menthods can capture the higher-order statistics. So the detection algorithms based on kernel methods have better detection performance.3. The images of infrared point targets are intensity images. Their features are not distinct. There is no texture features and the edge is dim. When the mean shift and particle filter algorithms track these targets, they often lose targets or the tracking precision is not high. We combine the proposed sub-space detection algorithms with Kalman prediction to track infrared point targets. This method has a high tracking precision. It can also be considered as a TBD method.4. At present, the tracking methods specially designed for infrared surface targets are not found. The mean shift can not gain satisfying performance when tracking them because of the poor feature. For resolving this problem, we propose a novel method for tracking infrared surface targets. The Gabor feature and entropy feature first are extracted from original infrared images. Then the feature images are used to synthesize pseudo-color image which is named as multiple feature image (MFI) by us. Thus the distinction between targets and background is more appreciable in MFI. The targets are tracked in MFI by mean shift which can complete the mode search with more features. So the tracking precision becomes higher. This method provides a novel idea which makes mean shift have a capacity of capturing more features besides the statistical feature of pixel intensity. It indirectly removes the deficiency of mean shift.