Research On The Target Tracking Application To Photoelectricity Platform

Since the19th century50's, the earliest prototype of aircraft-borne platform(people used kite and balloons to bring camera to the sky) was come out, and at thebeginning of the20thcentury people use airplane for photography investigation, andduring the world war II aviation camera as a form of aircraft-borne platform hadbecome a major air detection tools, then the late of20thcentury, aircraft-borneplatform had already had the function of image transmission, and target tracking,which had entered to another epoch, and today the present aircraft-bornephotoelectricity platforms have multitudinous functions such as investigation, andtrack, and navigation, and image real-time transmission, and Range finder, and theimaging device has changed from single optical camera to many kinds of forms suchas multi-spectral, infrared, microwave, and synthetic aperture radar, what's more, itsapplication area has involved into the video broadcast and retransmission, surveyingand mapping, resource investigation, environmental monitoring, patrolling, andgeological prospecting, scientific experiments in space, air reconnaissance, militarycombat, and many other fields.And target tracking technology as the key technology for airborne platform torealize those functions (target detecting, monitoring, tracking, and attacking), is anadvanced topic which syncretizes image processing, the pattern recognition, theartificial intelligence, the automatic control and many kinds of different domainadvanced achievement. This paper takes "the target tracking technology on the aircraft-bornephotoelectricity platform" as the research content, namely, main research is targettracking applied at the aircraft-borne photoelectricity platform, in this field, there arestill many challenges, such as image fuzzy caused by platform jitter, large anglerotating of object,3D affine transformation, large-scale zoom, change of the field ofview caused by the relative movement between target and platform, change of goal'sappearance, non-rigid deformation, partially or completely occlusion, and the targetacquisition after tracking failure, those are all the questions which urgently await to besolved, then the long-term stable tracking will come true. However, except the aboverobustness, another important issue is real time which is one of major indicatorwhether an algorithm can be used in platform.For the problem described above, many scholars make use of some immutabilityfeatures (such as SIFT, SURF) to realize feature extractions and matching in differentscales, and rotation; and some other ones research Mean-Shift theory and realize thetarget tracking under the case of target revolving, illumination change, part occlusion;and using the Kalman filtering, the particle filter theory, to realize forecasting targetposition in the situation of part or complete occlusion also is one means, but none ofthese algorithms can address all of the above-mentioned challenges, they are allsuitable in some circumstances, this paper focuses on research on these issues.The first is the study of the objectives of the modeling, presently; it can generallybe divide into two categories, one of which is invariability feature (which has goodrepetitiveness even in the situation of scale change and rotation), method of this kindin the step of extracting feature and depicting the feature cost huge computation, andthe feature matching step also spend more calculation; the other one is simple featurewhich do not pay much attention to the invariance of single feature, use statisticaltheory to make the simple feature which is easy to find and depict joint use, thenachieve the robust result, the second chapter mainly researched this two style featurewhich is target modeling.In chapter3, we did some research on target tracking with local invariancefeature matching. First, we use KLT and RANSAC algorithm realize image matching, which can be used in image stabilization; second, target tracking with SURF, and forhuge computation and long time taken by searching process of high dimension featuredepicter, we try to use expectations maximum (EM) algorithm to feature matchingstep, and achieved the very good experimental results.In Chapter4, this article focuses on research in semi-supervised learning whichis the part of the statistical learning theory, and elaborates the difference betweensemi-supervised learning, supervised learning and non-supervised learning, and theuse of unsigned data improving the learner performance; finally, on the basis ofstudying many theory, we propose a boostrapping feedback learning (BFL) method,and had proofed that once meet several conditions, feedback learn using unsigned datacan improve the performance at every step.Fifth chapter has the close relation with the fourth chapter, first, we userandomize fern and BFL combined realize target detection based on Fern-BFL, thenpropose a framework of general target tracking system, which subverts traditionalonly tracking or detecting and simple target model exchange mechanism. Then we useFern-BFL on its implementation, by3parts experiments we conclude that this targettracking system has good robustness and the capabilities of re-capture, and cantracking long-term and stably.A method using partical swarm optimism (PSO) accelerate Mean-Shift algorithmfor a real-time balance for a robust of tracking was researched in the sixth (6th)chapter, which achieves fast target tracking and satisfies the real-time request, throughexperiments we conclude that it's robust to object revolving, fuzzy, similar objectnearby.