Research On Moving Object Detection And Tracking Based On Feature Fusion And Sparse Representation

As the important research direction in the field of computer vision,Moving target detection and tracking has been widely used in the field of intelligent video surveillance system,object recognition and human-computer interaction and paid more and more attention in recent years.Therefore,it has important significance to achieve a high tracking accuracy and real-time target detection and tracking algorithm.This dissertation mainly to study moving target detection and tracking technology in the condition of the realistic scene with illumination change or occlusion.Then put forward the corresponding improvement methods,and compare the improved algorithm of this dissertation with the current popular algorithm in the detection and tracking parts and give the detailed results and analysis.The main research work of this dissertation as follow:The dissertation introduces the theory of feature fusion and sparse representation,and studies the technology of image pre-processing of moving target detection and tracking.By using image pre-processing properly can reduce the interference of external factors in the real scene effectively,which is useful for the follow detection and tracking.In order to solve that frame difference formed the hole and background subtraction is interfered by the background noise.This dissertation proposed a combination detection algorithm of frame difference and background subtraction with the adaptive iterative threshold processing.Study now more common moving tracking detection algorithm,and the improved algorithm introduce based on high real-time processing and strong illumination adaptability and detection moving target integrally of these two algorithm.Then the simulation experiments are made for video sequences in the different real scene environment and the experiments prove the improved algorithm has a better result.Due to the single feature extraction,the compression tracking cannot meet illumination and occlusion.This dissertation proposed an improved moving target tracking method based on feature fusion and sparse representation.The improved algorithm locates the position of moving target by search the moving target in the sliding window from coarse to fine to meet the real-time target tracking,and use sparse random measurement matrix in moving target to extraction two kinds of features.Then calculates the relative reliability between the two kinds of features based on the Bhattacharyya coefficients and updates the weights of two features are dynamically by Sigmoid kernel function regression map to achieve the purpose of enhancing the tracking accuracy.