Research On Tracking Technique Of Imaging Tragets In Ground Environment

Ground target tracking technique is an important part in the processing of imagery guiding heads in precise hitting weapons. Following the demands from practical applications of ground target tracking technique, this dissertation researches the problems of moving ground target tracking, ground multiple moving target tracking and matching-based ground fixed target tracking. The major parts of this paper are:First, the state-of-art target tracking methods for imagery sensors and their preliminary conditions are reviewed. Furthermore, the difficulties and demands in the research on ground target tracking are analyzed.Second, a novel Particle Filtering (PF) with Broad Interactive Genetic Algorithm (BIGA) is presented. The new method improves the performance on the problem of particle degeneration and particle shortage. Simulated experiments demonstrated that the presented method over-performs EKF, the traditional PF and the Regularized PF (RPF). Based on the presented method a ground moving target tracking procedure is implemented. Real-data experiments show that the procedure can cope with complex background clutter and short-time target occlusions.Thirdly, a novel moving multiple targets tracking method based on the visual attention model is proposed. The features on gray-level, detail and motion are extracted and then fused into a dynamic salient feature; moving multiple targets are detected with this dynamic salient feature based on the visual focus theory. Real-data experiments demonstrate that the proposed method has a robust performance.Fourthly, a dynamic salient PF (DSPF) target tracking method is presented. Combined DSPF with dynamic salient target detection, a robust ground multi-moving target tracking method is proposed. The new tracking method use dynamic salient features as the input to PF to estimate the target location; and associate with the dynamic salient target detection results to reveal the trajectories of the multiple moving targets in the view. Real-data experiments show that the proposed tracking method can cope with the target changes such as disappearing, merging, splitting and occulting.Fifthly, we proposed the maximum of gradient sum as the similarity measurement and a search strategy composed of the Genetic Algorithm (GA) and an improved "Gauss Sums" Gradient Neural Network (IG-GNN). Based on this similarity measurement and search strategy, a novel matching-based ground fixed target tracking method is presented. The experiments on synthetic optical and IR images show that the method is robust and ease to be implemented in practical applications.