Zha Y.-F.,PLA Air Force Aviation University |
Zha Y.-F.,Air Force Second Flight College |
Bi D.-Y.,PLA Air Force Aviation University |
Yang Y.,PLA Air Force Aviation University |
And 2 more authors.
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2010
In the problem for object tracking, most methods assume brightness constancy or subspace constancy. When both of the foreground and background are changed, the above assumes are violated in practice and the object will be lost. In this paper, the object tracking problem is considered as a transductive learning problem and a robust tracking method is proposed. In order to obtain good result, the object not only fits the object model but also has the same cluster as the previous objects. The previous objects are the labeled data and the candidates are considered as unlabeled data. The cost function is obtained with global and local constraints. Moreover, a novel graph is constructed over the positive samples and candidate patches, which can simultaneously learn the object0s global appearance and the local intrinsic geometric structure of all the patches. The solution for minimizing the cost function can be solved by the simple linear algebra with graph Laplacian. The proposed method is tested on different videos, which undergo large pose, expression, illumination, and partial occlusion, and is compared with state-of-the-art algorithms. Experimental results and comparative studies are provided to demonstrate that the proposed method works well with these situations and tracks the object robustly. Copyright © Acta Automatica Sinica. All rights reserved.
Liang X.-L.,Air Force Second Flight College |
Liang X.-L.,PLA Air Force Aviation University |
Hu J.-H.,PLA Air Force Aviation University |
Jing X.-Y.,PLA Air Force Aviation University |
And 2 more authors.
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | Year: 2010
Aiming at the multi-combat aircrafts attacking ground targets problem, a multi-stage optimization control model is presented, and the steps of the algorithm are given. The global optimal solution of the multi-stage optimization problem is obtained by this improved particle swarm optimization (PSO) algorithm, and the global constringency of this algorithm is analyzed. Simulation results indicate that the multi-stage optimization control model formulated in this paper is rational, and the PSO based algorithm is effective.