Improved Otsu Target Tracking Algorithm Based on Iteration
An improved maximum between-cluster variance (Otsu) method based on iteration was proposed for the purpose of realizing target recognition and target tracking effectively. Improved Otsu method was used to segment image collected by CCD, so as to extract the target from image and finish the target localization. By calculating centroid of the target in a single frame and extracting feature point of outline constituted by centroids of the single-frame target within unit interval, a polygon can be formed, the gravity center of which was taken as the target position. Filter model was established of which the control input is nonzero based on Kalman filter Principle, which was combined with target position at this moment can be used to predict the gravity center of the target at the next moment. The experiment and simulation results indicated that improved Otsu method can solve the problem that target and background are segmented mistakenly when image thresholding. Kalman filtering was able to predict target trajectory effectively after target being recognized. The requirement of target tracking was satisfied and target tracking was realized through this improved Otsu target tracking algorithm based on iteration.