Research On Extended Target Stable Tracking In Ground And Sky Background

Extended target tracking technology is an important part of the field of imageprocessing. There are strong prospects in real life. However, due to the diversity ofthe target and its background which makes no effective tracking algorithm can solveall problems. Simultaneously, it is a challenging task to develop efective andefcient appearance models for robust object tracking due to factors such as posevariation, illumination change, occlusion, and motion blur. While much success andclassical algrithms has been demonstrated, numerous issues remain to be addressed.Aiming at these problems, analyse the blocks and processes of the general targettracking algorithm, as well as difficulties in stabilizing extended target trackingtechnology, and studied two classical tracking algorithm’ advantages anddisadvantages, and on this basis, the stability of the extended target trackingtechniques were studied. Mainly includes three aspects:Firstly, in-depth study of the impact of the expansion feature target trackingalgorithm performance. Solve the problem of extended target tracking featureselection and fusion perspective. Many commonly features were introdeced. For asingle feature is insufficient to effectively target tracking problem, consider usingmultiple feature fusion tracking. Analyse the way of fusion, and on this basis, anonline combined two kinds of features method for visual object tracking wasproposed. A feature set was built by combining object contour’s linear parametricequation and edge histogram. Firstly, the Hough transform is used to detect line inpicture and obtain the parameters of the line, meanwhile, get the histogram at thesame position of the line. The resulting initial template contains the location andpixel distribution information of the target. Secondly, in the subsequent frames,pixels divided and clustered by gradient direction and parameter equation wouldform more than one line which was to be matched. At last, obtain the exact locationof the straight edge using the histogram matching algorithm. The experimentalresults indicate that the algorithm can run effectively when occlusion andcomplicated backgrounds happened, and perform favorably on challengingsequences in terms of efficiency, accuracy and robustness.Secondly, in-depth study of the template creation and updating. To solve theproblem from the perspective of the scale change. A modified initial position algorithm based on the weighted information entropy was proposed. At first, obtaintest samples in the search window. Then, calculate the weighted information entropyof each sample. Next, the sample is filtered out by priori information to obtain theminimum entropy region and the corrected target position. According to thedifferences beteen background and objectives to distinguish and verify theeffectiveness of the algorithm. Experimental results show that the algorithm is in thetarget complicated background can correct, reliable and stable correction of itsposition.For the problem of fixing template scale, use the SIFT feature matchingbetween two frames to obtained the affine transformation parameters which causedby changing in the template, thereby adaptively update the template scale. Test thealgorithm results under the framework of Meanshift. The experiment proved that,SIFT features can effectively solve the problem.Finally, in-depth study of the characteristics and effects of sparse appearancemodels for extended target tracking algorithm performance. From the perspective ofthe target appearance model describes the expansion of target tracking. Real-Timecompressive tracking was a simple and effective tracking algorithm. However, therewere a number of problems which need to be addressed. First of all, it was easy tointroduce errors due to factors such as occlusion and clutter. Secondly, it couldn’tupdate the positive and negative samples accurately while using fixed trackingwindow. At last, the number of testing samples was too large, which affected thespeed of tracking. The occlusion was checked by comparison between consecutiveframes’ histograms, and the coefficient can be also updated adaptively by thecomparison result. We searched for more specified areas with multi-scales to find outthe best matching place, and to handle scale change of the target on the basis of theoriginal algorithm’s tracking result. The different numbers of sub features sets wereutilized to filter the testing samples. In that case, the speed of tracking process wouldbe improved. The strategies we proposed would improve the original algorithm’sperformance to avoid the failure of tracking. The experimental results indicate thatthe algorithm can run in real-time and perform favorably against state-of-the-artalgorithms on challenging sequences in terms of efficiency, accuracy and robustness.Further more, In this paper, we propose an algorithm based on SIFT andcompressive features to develop effective and efficient appearance models for robustobject tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. The algrithm describe the target and background with compressivefeatures which labled as positive and negative specimens sampling from frames. Thetracking task is formulated as a binary classification via a SVM classifier with onlineupdate in the compressed domain. In new frame, utilize the classifier to abtain thetarger’s position. Meanwhile, introduce SIFT to solve the target size change, so as toachieve adaptive template size. The proposed tracking algorithm performs favorablyagainst state-of-the-art algorithms on challenging sequences in terms of efficiency,accuracy and robustness.