Air To Ground Object Tracking In Complicated Background

Object tracking belongs to computer vision. As one of the hotspot in the image processing, it plays a great important role in conventional warzone surveillance, TV guidance and intelligent monitor. Precision usually decreases during tracking in traditional tracking algorithm due to the rotation, scale transform and occlusions of target. Furthermore, tracking also can fail because of other change of observation information caused by illumination change, focusing and shake of camera. Above mentioned problems still exists though many scholars make a lot of effort on object tracking. Therefore, object tracking deserves to in-depth study.TV tracker is taken as the research background. This paper presents a framework for tracking by classification based on SVM(Support Vector Machine) with the analysis the disadvantages of traditional tracking algorithm such as geometric centroid tracking and correlation tracking. The ability of learning and relocking target is emphasized in this paper. Tracking algorithm in the paper comes down to features classification. The features are extracted in the tracking gate because the SVM has advantage on handling small samples data. The object in the new images can be confirmed by increasing the number of features in the tracking gate. In this way, object can be tracked accurately in the complicated background. And the new features can be got via online learning. It’s the same with model update in the correlation tracking.SVM is designed as a closed-loop algorithm based on computational learning theory. Training set used for training SVM is made up of object features. This target is designated through manual way or detecting algorithm in the initial video. The testing set is also features, but the object is from the last video. SVM will pick up new SV(Support Vector) from the testing set. And then, these SVs will be added into the training set for another training. In this way, SVM can study the target during the whole video and overcome the problem of the rotation and scale transform. SVM is good for multi-dimensional data, although it costs much time in calculating. Many features of object and background are extracted to form a multi-dimensional data as a row vector, including the line, edge, opposite angles and center. These row vectors comprise the training data. According to the influence of trees and fog in the air to ground tracking, the holistic Haar feature is added into the row vector. Target can be tracked accurately in the case of occlusion, and this is benefited by subtraction of Haar features.Kernel function plays an important role in SVM. It’s mainly used for two ways. One is to transform the input space into feature space whose dimension is higher than the other. The precision of classification is increased in this way and the parameter of kernel function can be confirm at the same time. The abstract classification become materialization via simulation with the help of IRIS. The other way is to evaluate the image in the video. Fast matching between image and data base come true on the basis of assessment result. The experiment proves that the real-time of algorithm is improved adopting the parameter confirmed by simulation, and the object can also be tracker accurately.This algorithm solve the rotation, scale transform and occlusions problems which are found in traditional tracking algorithm. The development tendency of object tracking is study at the end of the paper.