基于聚散熵及运动目标检测的监控视频关键帧提取  

Surveillance Key Frame Extraction Based on Aggregation Dispersion Entropy and Moving Target Detection

作　　者：马利克[1,2] 彭进业[1] 冯晓毅[1] 

机构地区：[1]西北工业大学电子信息学院,陕西西安710072 [2]陕西省人民警察培训学校,陕西西安710054

出　　处：《西北工业大学学报》2015年第3期462-466,共5页Journal of Northwestern Polytechnical University

基　　金：高等学校博士学科点专项科研基金(20116102110027);国家自然科学基金(61075014)资助

摘　　要：针对公安监控视频检索中根据运动目标准确标注视频关键帧的问题,提出一种基于聚散熵及运动目标检测的监控视频关键帧提取算法。首先通过对视频内容的分析,提出监控视频聚散熵的概念。其次根据聚散熵对监控视频进行子镜头划分,再次根据运动目标检测对子镜头进行划分,从而提取视频关键帧。最后列举出算法在几种典型视频数据库中的实验结果及结果分析。实验结果表明该算法在关键帧提取的准确性和鲁棒性上都有良好表现,该算法针对公安监控视频检索需求,在缩短公安视频侦查时间及智能检索中起到支撑作用。Key frame extraction is an important step in surveillance video retrieval. We propose a surveillance key frame extraction algorithm which is based on the aggregation dispersion entropy and moving target detection. Firstly, the concept of the aggregation dispersion entropy was defined to distinguish the presence of moving objects in video. Secondly, the aggregation dispersion entropy was used to divide surveillance video into several shots. And then the shots were splitted into subshots by the moving target detection. So the key frames could be got though the subshots. Finally, the algorithm of key frame extraction was given. The experimental results and their discussions were given;they showed that this algorithm has good performance both in accuracy and robustness for several different databases. Also, it is the demand of surveillance video retrieval in police use. And it is expected to be of further use in police video investigation.

关 键 词：监控视频 关键帧 聚散熵 运动目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]