《计算机应用研究》|Application Research of Computers

模糊加权的高效鲁棒人体动作视频检索

Efficient and robust video retrieval for human activity with fuzzy weight

免费全文下载 (已被下载 次)  
获取PDF全文
作者 张涵,韩毅,李跃新
机构 1.安阳工学院 计算机科学与信息工程学院,河南 安阳 455000;2.华中科技大学 国家数控系统工程技术研究中心,武汉 430000;3.湖北大学 计算机工程与通信学院,武汉 430000
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2019)03-064-0957-04
DOI 10.19734/j.issn.1001-3695.2017.10.1018
摘要 为了提高人体动作视频检索的鲁棒性和效率,提出了一种模糊加权的人体动作视频检索方法。该方法采用3D Harris算子检测视频中的时空兴趣点,提取这些兴趣点的梯度信息,构建特征向量;然后采用模糊聚类方法构建聚类特征向量,提高特征向量的抗干扰能力;匹配聚类特征向量中的梯度向量对,构建模糊权重矩阵,计算查询视频与数据库中各个视频的相似度;最后在KTH数据库上进行视频检索实验,结合精确度、召回率和检索耗时三个指标进行评价,证明该方法的有效性。
关键词 视频检索;行为识别;模糊聚类;时空兴趣点;3DHarris
基金项目 国家自然科学基金资助项目(64110712)
河南省科技攻关计划资助项目(1721021103)
本文URL http://www.arocmag.com/article/01-2019-03-064.html
英文标题 Efficient and robust video retrieval for human activity with fuzzy weight
作者英文名 Zhang Han, Han Yi, Li Yuexin
机构英文名 1.CollegeofComputerScience&InformationEngineering,AnyangInstituteofTechnology,AnyangHenan455000,China;2.NationalNCSystemEngineeringResearchCenter,HuazhongUniversityofScience&Technology,Wuhan430000,China;3.SchoolofComputerScience&Engineering,HubeiUniversity,Wuhan430000,China
英文摘要 In order to improve the robustness and efficiency of video retrieval for human activity, this paper proposed a fuzzy weighted method for human activity video retrieval.This method used 3D Harris operator to detect the spatio-temporal interest points in the video, and extracted the gradient information of these points to construct feature vector to describe video.Then it used fuzzy clustering method to construct fuzzy clustering feature vector, to improve the ability of anti-interference of feature vector.And then, it matched pair of gradient vector in the fuzzy clustering feature vectors to construct fuzzy weight matrix, and calculated the similarity between the query video and each video in the database.Finally, it carried out the video retrieval experiment on the KTH database, and carried the evaluation out with three metrics of accuracy, recall and retrieval time, which proves that the performance of this method is the best.
英文关键词 video retrieval; behavior recognition; fuzzy clustering; spatio-temporal interest point; 3D Harris
参考文献 查看稿件参考文献
  [1] 梁俊杰, 熊亚军, 余敦辉. 一种基于本体的视频检索技术研究[J] . 计算机工程与科学, 2015, 37(10):1940-1946. (Liang Junjie, Xiong Yajun, Yu Dunhui. A video retrieval technique based on ontology[J] . Computer Engineering & Science, 2015, 37(10):1940-1946. )
[2] Chaaraoui A A, Climent-Pérez P, Flórez-Revuelta F. Silhouette-based human action recognition using sequences of key poses[J] . Pattern Recognition Letters, 2013, 34(15):1799-1807.
[3] Chen Chen, Jafari R, Kehtarnavaz N. Improving human action recognition using fusion of depth camera and inertial sensors[J] . IEEE Trans on Human-Machine Systems, 2015, 45(1):51-61.
[4] Chaaraoui A A, Padilla-López J R, Climent-Pérez P, et al. Evolutionary joint selection to improve human action recognition with RGB-D devices[J] . Expert Systems with Applications, 2014, 41(3):786-794.
[5] Tang Jun, Shao Ling, Zhen Xiantong. Human action retrieval via efficient feature matching[C] //Proc of IEEE International Conference on Advanced Video and Signal Based Surveillance. Piscataway, NJ:IEEE Press, 2013:306-311.
[6] Jones S, Shao Ling, Du Kairan. Active learning for human action retrieval using query pool selection[J] . Neurocomputing, 2014, 124(2):89-96.
[7] Jiang Y G, Li Z, Chang S F. Modeling scene and object contexts for human action retrieval with few examples[J] . IEEE Trans on Circuits & Systems for Video Technology, 2011, 21(5):674-681.
[8] 李瑞峰, 王亮亮, 王珂. 人体动作行为识别研究综述[J] . 模式识别与人工智能, 2014, 27(1):35-48. (Li Ruifeng, Wang Liangliang, Wang Ke. A survey of human body action recognition[J] . Pattern Recognition and Artificial Intelligence, 2014, 27(1):35-48. )
[9] 冯家更, 肖俊. 视点无关的行为识别综述[J] . 中国图象图形学报, 2013, 18(2):157-168. (Feng Jiageng, Xiao Jun. View-invariant action recognition:a survey[J] . Journal of Image and Graphics, 2013, 18(2):157-168. )
[10] Sipiran I, Bustos B. Harris 3D:a robust extension of the Harris operator for interest point detection on 3D meshes[J] . The Visual Computer, 2011, 27(11):963-976.
[11] Fang Xiaoyu, Tian Yonghong, Wang Yaowei, et al. Pair-wise event detection using cubic features and sequence discriminant learning[C] // Proc of IEEE International Conference on Multimedia and Expo. Piscataway, NJ:IEEE Press, 2013:1-6.
[12] Cebeci Z, Yildiz F. Comparison of K-means and fuzzy C-means algorithms on different cluster structures[J] . Journal of Agricultural Informatics, 2015, 6(3):13-23.
收稿日期 2017/8/21
修回日期 2017/10/9
页码 957-960
中图分类号 TP391
文献标志码 A