Study On Object Tracking And Background Subtraction Algorithms

Although significant progress has been made in the field of computer vision, a lot of important problems in this field still need to be resolved. Among them, background subtraction and object tacking has received many researcher's attention in recent years. Background subtraction and object tracking is one of the fundamental tasks in many ap-plications, such as visual surveillance, human computer interaction and vehicle navigation. Significant progress has been made in background subtraction and object tracking during the last few years, and several important algorithms have been developed for solving this problem. However, due to the fact that background subtraction and object tracking is a challenging problem, more efficient and robust background subtraction and object track-ing algorithm is still need to be explored. In this dissertation, some popular algorithms in background subtraction and object tracking are studied and several new algorithms are proposed.Gaussian Mixture Models is one of the most popular models for background sub-traction. Although EM algorithm can estimate GMM parameters accurately, it cannot be applied to background subtraction directly. The main reason is that EM is not an incre-mental algorithm. To solve this problem, a new sufficient statistic vector based online EM algorithm is proposed. Experimental results demonstrate that our approach can effectively and efficiently estimate GMM parameters.K-Means algorithm is a cluster algorithm which is simple to implement and efficient to run. Elliptical K-Means is an extension of K-Means and can also be used to calculate GMM parameters. By exploring the close relationship between EM and K-Means, a sufficient statistic vector based online K-Means algorithm is presented. Experimental results show that the performance of the new online K-Means algorithm is comparable with that of the Robbins-Monro approximated online K-Means algorithm. The usefulness of the proposed two online algorithms in background subtraction is validated on real video data and the performance of them is analyzed using simulated data.Gaussian Particle filter is a semi-parameter realization of Bayes filter and has been successfully applied to object tracking application. To simplify the realization of GPF, we proposed a new particle sampling method which combines the sampling step with state prediction step by taking advantage of the Gaussian assumption and by exploring the linear structure of the system dynamic model. We proved that when the system dynamic model is linear, the new sampling method is equal to the combination of the original sampling step and the state prediction step. When the system dynamic model is nonlinear, we use Taylor method to approximate the nonlinear model with a linear model. The proposed sampling method can then be applied to the approximated linear model.To solve the stability problem of template based object tracking algorithm, we pro-posed a new similarity measure function and applied it to an adaptive template matching based object tracking algorithm. Comparative experiments demonstrate that the improved algorithm can track object stably even serious occlusion present.Finally we demonstrate that the performance of object tracking can be improved greatly by combining background subtraction as a preprocessing step.