面向视频数据的时空伴随模式挖掘算法  

Spatial-temporal co-occurrence pattern mining algorithm for video data

作　　者：张潇誉 于自强 刘承栋 李博涵[3] 靖常峰 ZHANG Xiaoyu;YU Ziqiang;LIU Chengdong;LI Bohan;JING Changfeng(School of Computer and Control Engineering,Yantai University,Yantai Shandong 264005,China;Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,Shenzhen Guangdong 518034,China;College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing Jiangsu 211106,China;School of Information Engineering,China University of Geosciences,Beijing,Beijing 100083,China)

机构地区：[1]烟台大学计算机与控制工程学院,山东烟台264005 [2]自然资源部城市国土资源监测与仿真重点实验室,广东深圳518034 [3]南京航空航天大学计算机科学与技术学院,南京211106 [4]中国地质大学(北京)信息工程学院,北京100083

出　　处：《计算机应用》2023年第8期2330-2337,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62172351)。

摘　　要：时空伴随模式是具有时空伴随关系的视频对象组合。为了从海量视频数据中快速发现符合查询条件的时空伴随模式,提出一种基于三重剪枝匹配策略的时空伴随模式发现算法——MPA。首先,利用已有的视频对象识别和跟踪模型对视频对象进行结构化提取;然后,对提取的连续帧中大量重复出现的视频对象进行压缩存储并构建索引;最后,设计基于前缀树的时空伴随模式发现算法,以快速发现符合查询条件的时空伴随模式。在真实数据集和合成数据集上的实验结果表明,与暴力搜索算法(BFA)相比,所提算法的效率提高了30%左右,且数据量越大,效率提高越明显。因此,所提算法能够快速发现海量视频数据中满足查询条件的时空伴随模式。Spatial-temporal co-occurrence patterns refer to the video object combinations with spatial-temporal correlations.In order to mine the spatial-temporal co-occurrence patterns meeting the query conditions from a huge volume of video data quickly,a spatial-temporal co-occurrence pattern mining algorithm with a triple-pruning matching strategy —Multi-Pruning Algorithm(MPA) was proposed.Firstly,the video objects were extracted in a structured way by the existing video object detection and tracking models.Secondly,the repeated occurred video objects extracted from a sequence of frames were stored and compressed,and an index of the objects was created.Finally,a spatial-temporal co-occurrence pattern mining algorithm based on the prefix tree was proposed to discover the spatial-temporal co-occurrence patterns that meet query conditions.Experimental results on real and synthetic datasets show that the proposed algorithm improves the efficiency by about 30% compared with Brute Force Algorithm(BFA),and the greater the data volume,the more obvious the efficiency improvement.Therefore,the proposed algorithm can discover the spatial-temporal co-occurrence patterns satisfying the query conditions from a large volume of video data quickly.

关 键 词：视频对象 结构化 时空伴随模式 索引结构 剪枝策略 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]