| For the past few years, the research on object tracking technique in color video sequences is in the ascendant. Moving object tracking has become an important research topic in many study fields, such as pattern recognition, image processing, computer vision and weapon control-guide, etc. It plays very important roles in military, traffic and biomedicine, etc, and it is of a wide development prospect as well.By making use of gray information in images, active shape model (ASM) usually performs non-rigid object localization and tracking. The model shows its better robustness compared to other localization and tracking methods because of the use of the prior knowledge. Combining color information in images with active shape model and extending the improved model to video sequences, this paper presents an algorithm for moving object localization and tracking in color video sequences. First landmark points are extracted for object silhouette by human-computer interactive mode, then the landmark points form into training sample set. Aligning all the shape vectors in the training sample set, the prior contour model is constructed based on principal component analysis (PCA) technique. The parameters of the prior contour model show the mostly variable mode of the shape. First the prior contour model is put initially in the image, and then the shape model and local color feature model are needed to be trained alternately. The shape model is iteratively changed until it converges to the object silhouette that a good overall match is obtained in a few iterations.Finally through comparison of test results based on the moving butterfly fish in color video sequences, the method presented in this paper combining with the color information shows effective results for object localization and tracking in color video sequences. And the determination method of initial position presented improves object localization and tracking efficiency remarkably. |
PDF Full Text Request