The difficulty of multiple targets tracking is how to quickly fulfill the target matching from one flame image to another and fix the position of the target. In order to accurately choose target feature information fo...The difficulty of multiple targets tracking is how to quickly fulfill the target matching from one flame image to another and fix the position of the target. In order to accurately choose target feature information for reliable matching, simplify operations under the reliable precondition, and realize precise moving objects tracking, an approach based on Kalman prediction and feature matching was proposed. The position of the target in next frame image was predicted by Kalman, and then the moving objects of two adjacent frames were matched by the centroid and area methods. When occlusion occurs, the best matching result was found to realize tracking by matching matrix algorithm. The simulation results show that the proposed method can achieve multiple targets tracking accurately and in real-time under complicated motion movements.展开更多
The traditional oriented FAST and rotated BRIEF（ORB） algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB...The traditional oriented FAST and rotated BRIEF（ORB） algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB（MORB） algorithm is proposed. In order to improve the precision of matching and tracking, this paper puts forward an MOK algorithm that fuses MORB and Kanade-Lucas-Tomasi（KLT）. By using Kalman, the object＇s state in the next frame is predicted in order to reduce the size of search window and improve the real-time performance of object tracking. The experimental results show that the MOK algorithm can accurately track objects with deformation or with background clutters, exhibiting higher robustness and accuracy on diverse datasets. Also, the MOK algorithm has a good real-time performance with the average frame rate reaching 90.8 fps.展开更多
基金Project(61172089) supported by the National Natural Science Foundation of China
文摘The difficulty of multiple targets tracking is how to quickly fulfill the target matching from one flame image to another and fix the position of the target. In order to accurately choose target feature information for reliable matching, simplify operations under the reliable precondition, and realize precise moving objects tracking, an approach based on Kalman prediction and feature matching was proposed. The position of the target in next frame image was predicted by Kalman, and then the moving objects of two adjacent frames were matched by the centroid and area methods. When occlusion occurs, the best matching result was found to realize tracking by matching matrix algorithm. The simulation results show that the proposed method can achieve multiple targets tracking accurately and in real-time under complicated motion movements.
基金supported by the National Natural Science Foundation of China(61471194)the Fundamental Research Funds for the Central Universities+2 种基金the Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation of China(20155552050)the CASC(China Aerospace Science and Technology Corporation) Aerospace Science and Technology Innovation Foundation Projectthe Nanjing University of Aeronautics And Astronautics Graduate School Innovation Base(Laboratory)Open Foundation Program(kfjj20151505)
文摘The traditional oriented FAST and rotated BRIEF（ORB） algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB（MORB） algorithm is proposed. In order to improve the precision of matching and tracking, this paper puts forward an MOK algorithm that fuses MORB and Kanade-Lucas-Tomasi（KLT）. By using Kalman, the object＇s state in the next frame is predicted in order to reduce the size of search window and improve the real-time performance of object tracking. The experimental results show that the MOK algorithm can accurately track objects with deformation or with background clutters, exhibiting higher robustness and accuracy on diverse datasets. Also, the MOK algorithm has a good real-time performance with the average frame rate reaching 90.8 fps.
