Video Scene Clutter Background Removal Technology

The target detection and tracking technology based on optical signal processing is one of the most important technologies applied in many advanced weapons. Since the optical detection and tracking technology deals with 3D space-time signal and is a kind of passive detection technology, it has a lot of features, such as concealment, anti-jamming and high precision tracking performance. At present, the pressure from military and others demands that target should be detected as soon as possible and as far as possible, so there are a great of research interests in studying of the detection and tracking technology of dim point moving target from image sequence.The dim target detection and tracking system can be divided into three parts, the image pretreatment, the target detection and the target tracking. Because of the influence of the wobble of the sensor and the clutter background, the pretreatment is necessary and also it's the research trend of this dissertation.The main contribution of the dissertation is the whole studying of the pretreatment technology of image sequence including dim point moving target. In this dissertation, the research trends for the problem have been introduced; the'dim'and'point'has been strictly defined in mathematics from machine vision and human vision; the ideal clutter suppression system based on clutter predication and the realization and evaluation of evaluation index has been studied, in succession the clutter suppression technologies have been researched. Firstly, the classic nonparametric algorithm has been analyzed in detail and systematically, for it's weakness that it cannot remove the non-stationary clutter ideally, Kalman filter algorithm for clutter suppression in 2D image signal has been built. Secondly, Fast Adaptive Kalman Filter is presented based on fast wide-sense stationary areas partition algorithm: limited combination and division algorithm based on quarti-tree algorithm, new taxis filter route algorithm which can break through the limitation of the necessity of pixel neighborhood of 2D filter and Laplace data model with two parameters which is perfectly suitable for the residual image of Kalman clutter suppression. Experiments show that the performances of fast adaptive Kalman filter is good.