复杂扰动背景下时空特征动态融合的视频显著性检测  

Video Saliency Detection Using Dynamic Fusion of Spatial-Temporal Features in Complex Background with Disturbance

作　　者：陈昶安 吴晓峰[1] 王斌 张立明[1] 

机构地区：[1]复旦大学信息科学与工程学院智慧网络与系统研究中心,上海200433 [2]复旦大学电磁波信息科学教育部重点实验室,上海200433

出　　处：《计算机辅助设计与图形学学报》2016年第5期802-812,共11页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61572133);高等学校博士学科点专项科研基金(20110071110018)

摘　　要：现有的运动目标显著性提取算法对具有树枝摇晃、水波荡漾等复杂扰动背景的视频处理效果较差,无法排除背景对显著目标提取的干扰.针对此类视频,提出一种基于时空显著性信息动态融合的目标提取算法.在空间上,利用简单线性迭代聚类(SLIC)超像素分割算法计算重建误差,得到每帧图像上完整的显著目标;在时间上,考虑到显著目标内部各像素具有运动一致性的特点,利用连续多帧图像的运动估计引入运动熵来表征,同时利用中心周边差的机制来区分目标和背景的运动;最后由于人的视觉系统对运动信息更敏感,根据时间显著性的大小设置动态权重进行时空显著性融合,得到最终能兼顾动静两种情况的视频显著图.在4个视频数据库上的实验结果表明,该方法能够较好地抑制复杂扰动背景对于运动显著目标提取的干扰,优于对比方法.In recent years, most existing video saliency detection methods failed to find salient regions in complexbackground with disturbance （e.g. waving leaves, rippling water, etc.）. In this paper, a framework based ondynamic fusion of spatial-temporal features for detecting video saliency is proposed. Firstly, the spatial saliencyof current frame is computed by using the simple linear iterative clustering （SLIC） algorithm. Secondly the motionentropy in multiple continuous frames is calculated to represent the motion coherence of salient object overtime. At the same time, center-surround difference is used to separate target motion from its neighbors. As thehuman visual system is more sensitive to the motion information, a dynamic fusion strategy is adopted to combinethe spatial saliency map and temporal saliency maps in this paper, such that the final saliency map can takeboth static and moving objects into account. The experimental results in four video databases demonstrate that theproposed method performs better than traditional methods in video saliency detection.

关 键 词：复杂扰动背景 简单线性迭代聚类 运动显著性 运动一致性 运动熵 动态融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]