Moving Target Detection And Tracking In Complex Environments,

The detection and tracking of moving object is a very popular subject in the area of Compute Vision,Image Processing,Pattern Recognition and etc, Lots of achievements had been made in recent years. It has been widely applied in military missile guidance,monitor and surveillances medical diagnosing,video retrieval and other fields. And that under the condition of battleground, the environment is very complex and varied , there are kinds of targets that appear and disappear with few rules. So the kernel of monitor and surveillance system is the robust algorithms of moving target detection and tracking. The paper mainly probe into methods of detection and tracking in complex environment. The major works and innovation of this paper include:1,Studying moving target detection and Background Model built under static background. We analyze the character of each algorithm in target detection by experiment, some algorithms have been improved. Paper improved a novel algorithm of background model based on LBP texture, and getting a well detecting result. Aimed at the advantage and disadvantage of each method on target detection analyzed before, we adopt an integrated detecting method combined the Symmetrical difference and Background Subtraction in the moving region. Using Symmetrical difference,Multi-gauss Background and Single-Gauss Background combined availably, improved the integrality of detection, conquered the miscarriage of justice of target's whistle stop.2,Studing a tracking method based kalman filtering. Analyzing target matching,kalman filtering and mean shift algorithms, Particularly the wide application in moving object tracking of kalman filtering and mean shift. Then realized the correlative tracking methods of them.3,Studying the moving target tracking methods resisting the occlusion. An improved mean shift algorithm based on moving prediction has been proposed, which import target modeled with color and LBP texture united histogram, using real-time object's position information to make target tracking more robust. Lastly paper propose an improved particle filtering algorithm based on mean shift, which adopt color and LBP texture united histogram-based target model, build a self- adaptively dynamic system model, use mean shift iterative to every particle to high probability position, so that reducing the number of particles and improving the real-time quality.