Research On Moving Object Detection And Tracking Algorithm

In the field of computer vision,the detection and tracking of moving objects is a hot research spot.This research involves many fields,such as image processing,pattern recognition,multimedia technology and so on.What's more,it is indispensable in many applications,including military,traffic,monitoring,artificial intelligence and others.This paper study the aspects of moving target detection and moving target tracking combined with the latest research achievements at home and abroad.Some shortcomings of the existing algorithm was improved to realize automatic detection and tracking of moving target.In the moving target detection,this paper studies the background subtraction method and the inter frame difference method.Then,in view of the defects of the two algorithms which cannot detect the slow moving target accurately,an improved algorithm based on dynamic background is proposed.The algorithm in the background subtraction method as the foundation,combined with the inter frame difference method to update the background,can effectively detect the slow moving objects.The application range of the algorithm is relatively narrow,so the paper proposes a fast adaptive detection algorithm based on background subtraction method.This algorithm can realize the intelligent update of the background model by computing the efficiency of the background model,and realize the adaptive adjustment of the parameters through the noise level of the image.This algorithm can adapt to the sudden change of the scene,restrain the generation of noise and ghosts.And the simple calculation and pixels parallel make it have good multi platform application ability.In the aspect of moving target tracking,this paper focuses on the improved algorithm based on mean shift algorithm.In research,the traditional mean shift tracking algorithm is not suitable for tracking fast moving target.So this paper proposes an improved algorithm based on mean shift algorithm combined with bandwidth adjustment.By comparing the Bhattacharyya coefficients,the algorithm can adjust the bandwidth of the kernel function,and it can improve the tracking of fast moving objects.Then on the basis of the algorithm,this paper introduces the Kalman filter algorithm and proposes an adaptive mean shift tracking algorithm based on Kalman filter using adaptive window and sub-blocking.The algorithm combined with Kalman filter and historical information to predict the maximum position of moving target.And the target and the candidate region feature model is divided into blocks.By adjusting the bandwidth of tracking window by Bhattacharyya coefficient,the fast moving target tracking in complex background is achieved.