Research On Moving Target Tracking Method Based On Sparse Local Invariance Feature

Moving target tracking technology is one of the important research field of computer vision, it also associated with other image processing and pattern recognition.In recent years, moving target tracking technology in many fields have been widely used, such as military, medical, and transportation, the study of motion tracking technology has important practical significance and broad application prospects.In this thesis, we will include an in-depth study on the basis of previous studies of moving target tracking and summarized the problem of moving target tracking technology currently problems are mainly:the camera image sequence obtained in target tracking, the prevalence of the target is obscured or target self-occlusion, target deformation occurs, the time complexity is high and so on. To solve these problems, this thesis based on existing research theory, moving target tracking algorithm based on sparse local invariant features described.In recent years, the theory of compressed sensing proposed moving target tracking gives a new way of thinking, a number based on compressed sensing theory of moving target tracking algorithm improves the real-time tracking, but tracking process, where the image of the target due to the presence of scale, rotation, light and other transformation, affecting the stability of the track. Theory of local invariant features for image scaling, rotation, affine transformation problem, a class of local invariant features and image description method has been proposed. Moving target tracking algorithm based on sparse local invariant features herein described, while the combination of compressed sensing, image local invariant feature introduces the theory. The main work of this thesis are the following points:(1) Researched the theory of local image features, focusing on the SIFT algorithm, SIFT algorithm is a local feature invariant theory of classical algorithms, image feature extraction algorithm SIFT stable, with scale, selection, rotation and affine invariant characteristics.(2) Researched the compressed sensing theory, compressed sensing is a core part of the theoretical framework of sparse signal representation, random measurement matrix remodeling design and signals. In this paper, we use random observation matrix SIFT feature vectors are mapped to low-dimensional space based on compressed sensing theory.(3) In-depth study of the moving target tracking algorithm, the algorithm summarizes the existing problems of moving targets, In view of the existing problems, moving target tracking algorithm based on sparse local invariant features described is proposed:the tracking task considered classification problem, SIFT feature dimensionality reduction to train classifiers trained classifier after the target to be tested for the next frame image.Experimental results show that moving target tracking algorithm based on sparse local invariant features described in this paper has a good effect on the anti-blocking property and real terms.