基于運動矢量的運動物體提取方法

為提高視頻運動物體提取的準(zhǔn)確性，提出了一種新的基于運動矢量信息的視頻運動物體提取方法。將與運動矢量信息相關(guān)的分割因子用于運動物體提取的判據(jù)，然后通過對每幀圖像構(gòu)建分割因子矩陣，對每一幀分割因子矩陣進行二維熵門限判決，實現(xiàn)對視頻序列中的運動物體的提取。研究結(jié)果表明該算法對于全局運動比較穩(wěn)定且運動物體的速度較慢的序列具有較好的提取效果，提取成功率達90%以上。

Abstract:

 In order to improve the accuracy of segmentation of motion object against the background，this paper proposed an algorithm of motion object segmentation based on motion vector information of macro block. The algorithm used motion vector information to generate segmentation factor，and constructed matrix of segmentation factor to each frame. Finally the 2D entropy threshold algorithm was chosen to judge the segmentation threshold in each frame，and completed the segmentation process of motion object in video sequences. The proposed algorithm has better extraction performance for the sequence which has stable global motion and slower speed of movement. The experimental results show that the successful rate of the motion object extraction is more than 90%.