Study On Moving Object Detection And Tracking Based On The ORB Features

At present, as one of the hot and difficult topics in the field of computer vision,moving object tracking has been widely used in the fields of military guidance,visual navigation, robotics, intelligent transportation and public safety, etc. Movingobject detection is the premise of moving object tracking; based on the relationshipbetween the object and the camera, moving object detection and tracking can bedivided into static scene and dynamic scene. Aiming at the need of more and moreactive tracking, moving object detection and tracking becomes particularlyimportant. Meanwhile, local feature points algorithms have got rapid developmentin recent years. As a result, the feature points matching algorithm method was usedto achieve the object detection and tracking in dynamic scenes.For the target detection in dynamic scene, as the camera have translation,rotation, scaling, tilting and other possible movements, linear affine parametermodel can not well described the camera movement when the camera rotatesseverely and there is the need to detect moving object in real-time for the videosequence. In view of these two points, we choose the rotation model of eightparameters which is more versatile to describe the pixels movement. First we adoptPROSAC algorithm to eliminate the possible outliers, and use the least squaresmethod for the optimal solution after getting the correct matching feature point-pair.Combined with the rotation model of eight parameters to solve the global motionparameters, we do motion compensation to the image and frame difference methodis last used to get a moving target. The experiments demonstrate that, this methodnot only remains the advantages of SURF but also raises the detection rate, and caneffectively detect objects while achieving real-time performance.For the target tracking in static scene, there exist two main drawbacks in theclassical mean shift tracking algorithm. One is that it cannot adjust the size of thetracking window automaticlly, and another is that it cannot continue to track thetarget when occlusion occurs. Inspired by the CAMSHIFT algorithm, we expandthe mean shift tracking algorithm and propose an improved mean shift tracking algorithm to solve the two problems. We perform a large number of experiments tocompare the proposed method with other advanced algorithms, and validate itsrobustness.For the target tracking in dynamic scene, ORB algorithm is used to do thefeature points matching for the target detected to track the target. The method isverified by experiments that it can effectively detect objects while achievingreal-time performance.The novelty of our algorithm can be summarized as:As to the shortcoming of SIFT algorithm with the tilt angle change of camera,we propose ORB algorithm to improve the target tracking performance.the feature point matching method was used to track the target. The algorithm hasbeen tested on real shot sequences and standard sequences, and the experimentsdemonstrate that the method can effectively detect objects while achievingreal-time performance.