Robust Tracking Methods Of Moving Object In Natural Environments

Contributed to the non-contact characteristic,visual-information-based object detection and tracking tasks can be achieved without interfering desired objects.The detection and tracking of moving objects under dynamic scenes becomes one of the most significant topics in computer vision research.In realistic application,the observed data sets often contain high percentage of outliers,which make it necessary to use robust methods for estimation.For any practical tracking algorithms,robustness of the tracker must be concerned.Recently,with the rapid development of computer hardware,the requirements of real-time computing are more dependent on hardwares' performace.Then,robust problem,which still remains very difficlut,has been more and more essential and critical in tracking.The research on how to improve tracking robustness in complex natual circumstance,not only give benifit to the achievement and promotion of intelligent automatic visual information processing,but also will further promote the comprehension of biological visual understanding mechanism itself.This dissertation discusses the theories and applications of moving object tracking through robustness requirements,robustness description,robust treatment strategies and robust tracking algorithms.We analysis the different sub-tasks of tracking,such as background modeling and background substraction,move estimation and move tracking,object tracking.And its different robustness requirements are taken into account.Traditional robust estimators are improved.New tracking tactic and algorithms,which based on robust statistics or biological technology,are presented. Experiments show that these algorithms compared with the existing algorithms,have an advantage on the efficiency,accuracy,robustness,and so on.The main tasks and contributions of this thesis are:(1) First,some fundamental concepts about estimation theory are introduced.The statistical definitions of robust estimator(such as outliers,breakdown point and influence function) are given and some robust statistics methods(such as Median, M-estimator,RANSAC) are discussed.Their respective advantages and limits are examined.Nonparametric statistics has a close relationship with robust estimation.We also analysis some nonparametric estimation method,focus on mean-shift's performance and convergence,and give experimental verification. (2) Taking the task of background modeling as an example,its robustness requirements under different conditions are analyzed.We consider the reasons of noise in different applications;give a structural analysis of its sample set.To dealing with multiple structural contaminated data,the traditional RANSAC is improved by MAD estimator and multi-procession.On this basis,a novel robust background modeling algorithm is presented.The model is established by an improved Multi-RANSAC approach for dynamic background pixels and by one-tail trimmed mean estimation for static pixels.A three-component cell is derived for the model so that it can be updated quickly in a unified framework.The method effectively overcomes the inherent deficiency in RANSAC and can tolerat a high percentage of oulier.It proves right even when there are more than 70 percent outliers and is fit for extraventricular natural scenes.Quantitative evaluation and comparisons with GMM estimator and Median estimator verified that the proposed method has much better performance.(3) M-estimator is one of the most important robust estimators,which can achieve different performance of robustness by choosing different loss function.In the process of estimating and tracking of moving object,the traditional Kalman filter-based method can't overcome the influence of outliers,and has trouble in the condition of nonlinear outliers and gross outliers.Aiming at this problem,we combine the Turkey M-estimator with Unscened Kalman Filter and present a new M-UKF tracking algorithms.It can not only solve the problem of estimating the nonlinear effection,but also overcome the interference of gross outliers.Simulation and experiments show its validity.(4) After conducting a comprehensive inspection of object tracking process from the perspective of biological visual cognition,we develop a robust intelligent tracking tactic based on the intrinsic and extrinsic features of frog's visual system.It is achieved through a procession called as "fuzzy region understanding".Mean-Shift based-method is applied and the effect of scale-parameters is discussed.An adaptive local scale-parameter adjustment is presented.The proposed method has been applied in the sences which may have gross noises such as local scene break or object appearance variation or interference with movement.Experiments and comparisons with two traditional algorithms demonstrate the validity and robustness of our algorightm.