Research On Computer Vision Based Moving Object Tracking

With the rapid development of computer theory, technolgy and application, the video image processing capacity and computer's performance have been greatly improved. It made computer vision became a very popular research task in the computer science and artificial intelligence. Computer vision based moving object tracking is a very important challenge in computer vision field. Object tracking based on the computer vision is a technology of detection, extraction, recognition and tracking moving object from video image sequence and get the moving parameters such as position, velocity, acceleration and object's moving trajectory. Based on these parameters, with deeply process and analysis to achieve behavior understand and complete advanced task. Object tracking based on computer vision as a widely used technolgoy, has attracted many researchers. Lots of institutions take it as a very important research field and have got many remarkable achievements. But computer vision based object tracking technology is still not a mature technology. There still have a great number of problems which should be solved to develop a robust and practical tracking system.Thesis focuses on feature-based object segmentation technology, template matching technologyand Mean shift object tracking technology. On real sequence image scene analysis, taking the task of acquire rocket's trajectory and flight regime when rocket launch and fly as research background. It using high-speed video camera directly to trace and locate the rocket image. Meanwhile, it takes deeply research and discussion on rocket tracking based on different scenes.Thesis discusses the current research of computer-vision-based object tracking. The object representation method and tracking features choosing method in the computer-vision-based object tracking filed is also discussed. It classifies the algorithms of moving object tracking and points out the advantages and disadvantages of these algorithms. Thesis introduces and analyzes the theory frame of computer vision. Based on the Marr's computer vision theory frame, our approach is presented. It focuses on the analysis of rockets and backgrounds features under computer-vision-based rocket tracking scene. Based on that, the difficulties in computer-vision-based rocket tracking are proposed.In order to solve the rocket tracking problem this thesis compares the edge detection results using the different edge detection algorithms and chooses Roberts edge detection operator to detect the rocket edge. Improved OTUS segmentation algorithm is proposed according to distribution of grey scale and that enhances the accuracy and real-time performance of rocket segmentation. To solve the edge interferences in edge images, a nonlinear filter with direction is designed to remove background edges. Simulation has proved the proposed rocket method is effective for rocket segmentation.Thesis proposes a multi-correlation-template match strategy to overcome the flaws of traditional template matching algorithms which cannot trace the targets with long time and stability due to the lack of adaptive template update for the change of rockets size and profile. Through affine transformation, the algorithm would generate multi-template to match the size and profile of the target rocket from optimal template in previous frame according to stretching and rotation. Thus the match accuracy is enhanced. To raise the real-time performance of the algorithm,Kalman filter is used to estimate the motive track of rocket, and the estimation reduces time complexity of the algorithm effectively. Simulation shows that the algorithm is robust to changes of target size and profile as well as block problems with good match accuracy and real-time performance.Thesis proposes an improved Mean shift which is acquired with combination of normal Mean shift algorithm and frame-difference methods based on the study of Mean shift algorithm and traits of rockets flying. By using Frame-difference method to exact the motive area of a rocket, we get its approximate location. Then with Mean shift algorithm, accurate tracing is achieved. Simulations prove the effectiveness of the new algorithm that it is effective to trace a target rocket and better solves the problem of error accumulation during the tracing.Thesis proposes an online multi-features fusion algorithm and adaptive template update mechanism to solve the defect that tracking template cannot be self-adaptive to various scenes which cause template drift, and that single image feature in Mean shift algorithm framework cannot be self-adaptive to trace scene to alter the best trace feature for a target rocket. This new algorithm considers a feature with greatest difference between target rocket and background as the best trace feature. Thus, rocket and the background are classified into two different classes, and a function is established to measure the differences between target rocket and background. To acquire a complete outline, the different features are combined through ratio. By establishing a similarity function of two frames before and after, tracing templates are updated adaptively. Simulations show that this new algorithm is effective in tracing target rocket with complex background.At last, the thesis summarizes all research work discussed above, and points out the further research direction in this field.