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首页> 外文会议>2010 2nd International Conference on Image Analysis and Signal Processing (IASP' 2010) >A similarity analysis and clustering algorithm for video based on moving trajectory time series wavelet transform of moving object in Video
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A similarity analysis and clustering algorithm for video based on moving trajectory time series wavelet transform of moving object in Video

机译：基于视频中运动物体运动轨迹时间序列小波变换的视频相似度分析和聚类算法

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摘要

Time-series data mining is a very important element of data mining. As a typical time-series data, video data has been widely used for many applications such as film, medical, sports and traffic areas. In this paper we integrates several technologies into the video moving object time-series data mining. We propose a similarity analysis and clustering algorithm for videos based on the moving trajectory time series data wavelet transform of the moving object in the videos. Utilizing the image difference technique, our algorithm detects the moving objects from the scene surveillance video, calculates the centroid of the moving object, and uses the centroid series to character the moving trajectory of the object. Then we use wavelet analysis method to achieve dimensionality reduction and get the first k wavelet coefficient to substitute the original motion time-serial data. Based on the Euclidean distance, we utilize two judgement rules to determine the similarity of time-serial data and cluster them, use the rules to perform the similarity search and clustering of video. We apply the algorithm to athletic sports videos analysis.
机译：时间序列数据挖掘是数据挖掘的一个非常重要的元素。作为典型的时间序列数据，视频数据已广泛用于许多应用，例如电影，医疗，体育和交通领域。在本文中，我们将多种技术集成到视频运动对象时间序列数据挖掘中。基于视频中运动物体的运动轨迹时间序列数据小波变换，提出了一种视频相似度分析和聚类算法。利用图像差分技术，我们的算法从现场监控视频中检测出运动物体，计算出运动物体的质心，并使用质心序列来表征物体的运动轨迹。然后利用小波分析方法实现降维，得到第一个k小波系数来代替原始运动时间序列数据。基于欧几里得距离，我们利用两个判断规则来确定时间序列数据的相似度并将它们聚类，然后使用这些规则执行视频的相似度搜索和聚类。我们将该算法应用于体育运动视频分析。

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