Monte Carlo Filtering For Target Tracking

To track target accurately and robustly, this paper chooses Monte Carlo filtering algorithm which is free from target motion model and noise. Have been referred to a great deal of overseas or domestic literatures, and many simulator experiments, deeply analyze and research on the fundamental theories of Monte Carlo Filtering. Traditional Monte Carlo Filtering has two fatal problems——the large computational load and samples degeneracy. To solve these two problems, new methods are presented.Good model has a key position on tracking problem. Traditional model could not resist the interference of background. So, a new model building method which is based on the outline is proposed. Firstly, get the target outline which is in a given region through a set of steps such as edge detection, line detection and so on. Describe the target using the information which is inside the outline. Secondly, describe target feature combining color and shape information. So the feature description can resist the change of backlight and target itself. Experimental results show the time of building model less than 300*300 pixels is less than 1 millisecond using VC++, which could be almost ignored. The matching time using new model is greatly decreased than that of using original model.Traditional Monte Carlo filtering algorithm has the problem of sample degeneracy. To solve this problem, this paper proposed samples building method based on visual principle and samples propagation method using half sampling and half re-sampling. At original frame, build the samples according to the distance from the center. Get denser samples where near the center and get sparser samples where far away from the center. During the samples propagation, classify the samples into good samples and bad samples. Re-sample the good samples for propagating and get new samples to replace the bad samples. Experimental results show the sample degeneracy problem is solved by this method.Mean Shift deals badly with non-linear problems and Monte Carlo method increases the computational load greatly. In order to solve these problems, a new target tracking method that chooses Mean Shift and Monte Carlo method to track object adaptively is proposed. Introduce a sign that denotes the tracking method to this article for choosing Mean Shift and Monte Carlo. The sign is confirmed by the match degree of current target and the model. When the match degree is above a given threshold, real- time Mean Shift method which is based on gradient method is chosen to track the target fast. Otherwise, Monte Carlo method which is based on random sampling and free from target model is chosen to track the target exactly. The basic theories and the whole frame of the tracking system are given. Experimental results prove that compared with Monte Carlo, proposed method has the same tracking performance and costs less time. When the target moves linearly, the average time is 82 milliseconds for Monte Carlo and is less than 1 millisecond for proposed method when the target is 100*56 pixels. Compared with Mean Shift, proposed method is more robust to non-linear problems.