Research On Particle Filter Target Tracking Algorithm For Video

Moving object tracking is one of the key subjects in computer vision and it combines the advanced technologies of pattern recognition, artificial intelligence, image processing etc. Object motion parameters can be obtained through analyzing the image sequences from the videos, and the parameters can be fed back to tracking system as the basis for video sequence analysis and understanding.The accuracy and real-time performance of moving target tracking system directly affect the accuracy of the subsequent video sequence analysis and understanding, and it is the key indicators of intelligent video surveillance system. How to improve the accuracy and real-time performance is the research focus and it has important application background.Particle filter is one of the moving target tracking methods which are generally used. So, improved algorithms are presented in this paper to remove the defects of the basic particle filter target tracking algorithm. The main work and results are shown as follow:1. In the basic particle filter target tracking method, a lot of particles will run away from the moving target, when the color of background is similar to the moving target. An improved particle filter target tracking method using accumulation histogram is presented in this paper. In this method, accumulation histogram is used to describe the target features instead of general histogram. Accumulation histogram reflects the relationship between the distance in color-axes and the similarity of color distributions, which distinguishes colors more accurately.2. In the basic particle filter target tracking, if we use large number of particles, the real-time of tracking is bad, and if we use small number of particles, the tracking errors become large. In order to overcome this defect of the basic particle filter, an improved particle filter target tracking method based on particle position adjustment is presented in this paper. In this method, every particle is made full use of to reflect target position more accurately, and better tracking results can be realized using fewer particles. The accuracy and real-time performance can be improved in this method.3. We expand the particle filter target tracking method based on particle position adjustment and realize a multi-target particle filter tracking method. Multi-target particle filter tracking is transformed into many single target particle filters tracking in the tracking process, the same number of particles is assigned to each particle filter respectively, and the total number of particles increases with the number of targets exponentially. So if we can reduce the number of particles, lots of the system time will be saved and the tracking efficiency will be improved quickly.We do experiments to verify the algorithms. The experimental results and data show that these improved methods have more accurate tracking results than the basic particle filter tracking method and they are effective methods for target tracking.