|其他题名: ||Research on the Technique of Moving Target Detecting and Tracking in Complex Environment
|英文摘要: ||Moving target detection and tracking in complex environment have been the key technique of the optical-electro detecting system. Aiming at the key issues of the dissertation, including theory, algorithm and hardware implementation of moving target detection and steady tracking, elaborately analysis and study were performed. The main objective of this dissertation is to develop some effective algorithms to solve some difficulties.
Based on the study of the characteristics of target that image background is always complex and changeful while target’s motion is highly maneuverable, target is difficulty to be detected with the general still image segmentation methods. Our research started with dynamic image analysis theory, at the stage of target searching, without regard to the change of illumination condition, the reason that resulting in change of image intensity between frames was exhaustively discussed, and educed an important conclusion that camera motion and dithering were the main reason of image intensity change. The transformation model of 3D motion field to 2D image velocity field on the basis of camera model is seriously analyzed, and obtained an useful conclusion that affine model can well simulate scene motion in the target detection system, which provided theory for later algorithms.
In order to improve video quality and performance of target tracking, after a series of experimentation on traditional methods, a new electronic stabilization algorithm was proposed based on recursive Kalman filter and global motion estimation. Motion vector was detected by feature point extraction and corresponding algorithm. Affine motion parameters were calculated through least square method. Recursive Kalman filter was applied to separate the dithering parameter from global motion parameter through considering camera motion as a stochastic process. Experimental results indicate the algorithm is effective and robust after extensive experiments over a wide variety of videos. In the other hand, PSNR and MSE showed quantificationally that the proposed algorithm is effective. Moreover, real time process requirement was satisfied by the algorithm.
As for target detection, a new algorithm based on background motion compensation was put forward that affine model simulates camera motion in the scene, and then obtain a precise motion compensation frame after robust estimation affine parameters through multi-scale motion estimation algorithm, where affine parameters were adjusted by Levenberg-Marquardt algorithm at each resolution, which translated target detection problem of moving camera into the problem of still camera through extruding target and removing background. Difference image after compensation was detection through hypothesizing and testing. After a series of morphological process, we obtained intact moving object. Experimental results indicated that this algorithm can effectively eliminate background motion and segment object in complex environment.
Robust and steady target tracking is another important issue of this dissertation. After analyzing the shortcoming of general template match tracking, a new tracking framework was constructed based on particle filter, which regarded nonlinear and non-Gaussian target tracking as state estimation parameter, particle filter implemented the process though Bayesian reasoning and Monte Carlo theory. According to the framework, kernel histogram was introduced as a measure model and integrated into particle filter tracking algorithm. Compared with template match tracking through simulated moving target track experiments, the new algorithm had a good advantage over template match algorithm through bartering a litter bigger computation and approximate track error for steady target tracking, moreover, it can also track the target with variational illumination owing to kernel histogram measurement.
As for two main problems in robust and steady target tracking, the description of target measure model and the technique of template update were meticulously studied. Mixture Gaussian model was introduced into particle filter tracking framework to be an effective target description, which was compose of three components and online updated with incremental EM algorithm. Many experiment results show that the algorithm can also track targets under variational illumination, especially for fast varying appearance and pose and local occlusion, furthermore, it is simple enough to be a real-time object tracking algorithm without resample.
At last, target detection and tracking platform based on DSP+FPGA with CPCI architecture was introduced. The particle filter tracking algorithm was implemented on FPGA through algorithm optimization. Experiment data proved the real-time algorithm.
In conclusion, some innovated methods were developed to solve the difficulties for target detection and steady tracking in complex environment. The work of this dissertation has important reference to the future development of the optical-electro detecting system.|
|Appears in Collections:||光电技术研究所博硕士论文_学位论文|
|File Name/ File Size
赖作镁. 地物背景下运动目标检测与跟踪技术研究[D]. 光电技术研究所. 中国科学院光电技术研究所. 2007.