Study Of Image Target Tracking Algorithms

Target tracking algorithm is refers to a process to determine the position of image goal which is in the image sequence or in a section of video in real-time, according to the needed target. On the basis of study and simulation of many kinds of tracking algorithm, we put the emphasis on three kinds of target tracking algorithm, seeking for new solution to different question existing in the tracking algorithms. It will build the foundation for further research and application in tracking algorithm.This paper has studied the gray template related matching track algorithm, on the gray image's simple target tracking question. It has analyzed the question to adapt template size automatically, and one kind of method which is based on the image information content is proposed in this paper to renewal template. In this paper, we combine this method with the normalization related algorithm with subtracting average value to track targets. The algorithm has made good progress in the experiment. In view of the problem of missing targets caused by the similarity between the gray of goal and background, PCA tracking algorithm combined with adaptive kalman filter is proposed in the paper. Simulation results show that our algorithm has good capacity to track targets, also it has good anti-noise and anti-degradation ability. The particle filter uses set of particle to represent the probability and can be used in any form of state space. This method does not request the system to be linear and can be used in the non-Gauss situation, there isn't concrete supposition to the noisiness. It can be applied to many situation, and it surpass linear filtering methods based on the recursion structure, such as Kalman filter and so on. For so many advantages, a tracking method based on particle filter is proposed in the paper. Depending on the target's gray and gradient feature, we make template with adaptive elliptical deformation to track targets in the gray image sequence. Create state transition model and observation model of the system, using re-sampling technique, use particles with high weight to replace particles with low weight, similar to ensure a set of robust particles, finally use the weighted sum of particles to estimate the ultimate position and shape of the goal. The simulation result shows that the algorithm proposed in the paper is effective and robust.