Research On Moving Objects Detection In High-Speed Image Sequences Based On Parallel Processing

Moving objects detection in high-speed image sequences is one of the most challenging techniques in computer vision, which is widely used in military, aerospace and industry. For the great value in theory and practice, various new theories and algorithms have emerged continously. So far, there are still many dificulities in practice. Specially, with the improvement of application requirements, as well as the rapid development of imaging technology, the system data are increased largely. How to solve the contradiction between real-time and precision is a key problem of moving objects detection in high-speed image sequences. The objective of this thesis is to research deeply some key problems for moving objects detection in high-speed image sequences focusing on real-time moving objects detection algorithms and parallel system structure design.After consulting a large number of domestic and foreign related literatures and references, this thesis provided an overview of moving objects detection method. Time domain, airspace domain and time-space moving objects detection method is described, and the characteristics of different methods are analyzed. Furthermore, image parallel process techniques, image sensor motion models and the current structural design of moving objects detection system are reviewed, and the characteristics and applied occasionsof the different structures were analyzed. Based on the existing moving objects detection technique theoretics, with the data characters of high-speed image sequences, the parallel system structure of high-speed image sequences moving objects detection system are designed.In view of the influence of various noises and the brightness change for mass acquired image data of the system, the partially overlapped idea is used in the process of image pre-process. An overlapped image pre-processing algorithm with integer wavelet transform and histogram equalization (WHI) is presented. Each high frequency transformed by integer wavelet is denoised by point-to-point BayesShrink thresholding and median filter to reduce the fluence of Gaussian noise, salt and pepper noise to image quality. Furthermore, brightness preserving histogram equalization is used in low frequency band to solve the problem of uneven brightness. Because of the overlapped image preprocessing algorithm, sub-images are needed to be interpolated by bilinear interpolation to solve blocking effects. Testing results proved that the algorithm have higher Peak Signal to Noise Ratio (PSNR) than tradition image pre-processing algorithms. The less time and the fewer memory space are needed. It avails the hardware realization of WHI algorithm. The image sensor needs to move with moving objects during the course of the high-speed image sequence data acquision. The perturbation motion of image sensor effects directly the accuracy of moving objects detection results. In order to avoid the global motion in image sequence, the matching block pre-judgment is used. The three parameters model background motion compensation algorithm with parallel initial position predict (PCQHBS) is presented. Above all, a gradient-based block prediction strategy is used to reduce the block number participated in matching. Then, the parallel prediction of initial search point, half-stop criteria and adaptive search modes is used in rudimentary cross-hexagon-searching algorithm to estimate motion vectors. The background motion compensation with three parameters model is used. Experiments proved that the algorithm have higher PSNR and speed than tradition background motion compensiton algorithms. Moving object areas can be chosen in the compensated image sequences by symmetric difference and projection technique.In order to acquire the accurate moving objects contour informations, the idea of labeled contour points is adopted, based on the traditional Snake model. A part-optimimum Snake model contour detection (LP-Snake) algorithm with labeled contour points is represented. The two outer energy functions are added into traditional Snake model: edge energy and centripetal energy. It can be devided into three phases: initial phases, building phases and approximation phases. The different energy functions and contour orthocenter determination method are used in three phases of contour detection. Experiment proved that the actual computed contour points are reduced, and the contour detection speed can be accelated.In order to detect moving objects in high-speed image sequences effectively and reduce the memory space of the system, the task is assigned into several FPGA or DSP according to parallel system structure. Hardware experiment of the key modules of each subsystem proved that the operating speed of the system is increased, and the memory space of the system is reduced effectively. The results validate the correctness and effectiveness of high-speed image sequences moving objects detection system.