Papers by Author: Shu Min Fei

Abstract: The alternating direction method has been widespread used to solve multi-area economic dispatch problem. Compared with traditional centered economic dispatch, alternating direction method divides centered optimal problem into completely independent sub-problems while the corresponding equality and inequality constraints are satisfied. However, plenty of applications show that the choice of penalty parameter for the consistency constraint has an important influence on the convergence performance of alternating direction method. In this paper, we proposed a novel improved alternating direction method. To be more exact, the key is to adjust penalty parameter based on iterative information of alternating direction method. Numerical results illustrate the proposed method has better stability in convergence.

Abstract: A new approach is proposed to detect and track the moving object. The affine motion model and the non-parameter distribution model are utilized to represent the object firstly. Then the motion region of the object is detected by background difference while Kalman filter estimating its affine motion in next frame. Center association and mean shift are adopted to obtain the observation values. Finally, the distance variance and scale variance between the estimated and detected regions are used to fuse the observation values to acquire the measurement value. To correct fusion errors, the observable edges are employed. Experimental results show that the new method can successfully track the object under such case as merging, splitting, scale variation and scene noise.

Abstract: To handle occlusion and accumulation error in tracking procedure, a novel method is proposed. Firstly, the object feature is modeled with kernel based color histogram. Then, mean shift is used to localizing the object with Kalman filter providing initial iteration location and scale. Object observation value is acquired by weighting the similarities of hue and saturation in x and y-directions. Finally, occlusion and scene disturbance are judged by maximal similarity and the matching deviation, so as to selectively update the object model. To suppress the accumulation error, the noise covariance is updated according to the iteration error in the latest N frames. Experimental results show that the proposed method is robust in tracking the occluded objects under complex scene.