Research On The Moving Targets Detection And Tracking Methods Under The Dynamic Scenes

Computer vision is an important field in computer science, and the object detection and tracking in image sequences is a very important research topic in the field of computer vision. Most of the the traditional object detection and tracking is studied under the fixed camera, but along with the diversification of monitoring, appearing more and more the moving camera, such as aerial camera, vehicle equipment, robot, handhold devices, and so on. When the background motion, object detection and tracking method in fixed background is no longer valid. So the research for object detection and tracking in mobile scenarios is very necessary. This paper aims to the targets detection and tracking research under the dynamic scenes, discusses the principle of object detection and tracking and effective algorithm, has important academic significance and application value.Study on the present status of object detection and tracking is reviewed. For the mobile camera movement under the background due to the difficulty of object detection, we used one kind of feature points matching method based on the SURF algorithm to compensate the movement of background, so as to realize the detection of objects using frame difference method. When filtering the features, this paper creatively combines the feature points motion vector with a grid, in which way can not only remove the mismatch feature points, but also the ones on the target. In this way, the accuracy of global motion parameters has improved.For the object tracking under the dynamic scenes, the algorithm is improved based on the popular TLD algorithm, which includes tracking, detecting, combining and learning. For the problem the object recognition rate is not high when there is large deformation or drift, this paper improved learning algorithm, to improve the recognition rate by increasing the tracking module to achieve accelerated algorithm of learning; and use the Markov mode to forecast the target moving direction and the optimal template matching search mode. Finally, the experiments verify the feasibility of the system, through the analysis of experimental results, achieved good results.