| 摘要: |
| 作为基于内容的视频检索与管理中的一项关键技术,视频镜头边界检测近年来得到了广泛的研究与应用.但是,传统的视频镜头边界检测算法只能处理切变镜头边界,渐变镜头边界的检测结果则远低于人们的期望.结合自适应阈值与傅里叶函数拟合技术,提出了一种用于检测切变与渐变镜头边界的方法.该方法首先计算视频中每一帧在HSV颜色空间上的非均匀分块直方图,再根据直方图距离得到视频数据的相似度序列,然后在其上使用自适应阈值法计算出相应的阈值,从而检测切变镜头边界.渐变镜头边界处较为复杂,通过大量的实验观察发现,渐变镜头边界处存在着特定的变化模式,并在相似度序列上寻找具有这种变化模式的片段作为候选边界,最后用傅里叶函数拟合形成统一的表示.收集不同类型的渐变边界,训练了一组标准渐变模型,用于判断候选边界是否为渐变边界以及确定其渐变类型.为了提高计算效率,采用通用并行计算架构对算法进行加速.通过大量的实验测试以及与他人方法的对比,验证了算法的有效性. |
| 关键词: 镜头检测 切变边界检测 渐变边界检测 自适应阈值 傅里叶函数拟合 |
| DOI： |
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| 基金项目:国家自然科学基金(61073098, 61021062); 国家重点基础研究发展计划(973)(2010CB327903); 江苏省自然科学基金(BK2009081) |
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| Shot Boundary Detection Using Adaptive Threshold and Fourier Fitting |
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LIU Feng1,2, GUO Yan-Wen1,2, YIN Kun-Yan1,2
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1.State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210046, China;2.Department of Computer Science and Technology, Nanjing University, Nanjing 210046, China
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| Abstract: |
| As a key technology in content-based video indexing and management, the video shot boundary detection has attracted considerable research attention. However, the traditional detection algorithm can only handle a hard cut boundary. No satisfactory results have been achieved on gradual transition until now. A method based on the combination of adaptive threshold and Fourier fitting is presented in this paper to detect shot transition. A non-uniform histogram in HSV color space on each frame is first accumulated to calculate a similarity sequence of videos, depending on which thresholds are generated by the adaptive threshold to detect hard cut transition. Gradual transition has a much more complex transform in its span. After extensive experiments and observation, the study has found that fixed patterns exist in every gradual transition. Candidate transitions are detected by finding segments which show traits of a gradual transition pattern. Gradual transition boundaries of different types are collected to train a set of standard templates, which can be used to judge whether a candidate is a real gradual transition boundary and also, to further determine its transition type. The algorithm is accelerated by exploiting the parallel computation power of GPUs using CUDA. The effectiveness is verified through extensive experiments and is compared with other methods. |
| Key words: shot detection hard cut detection gradual transition detection adaptive threshold Fourier fitting |
