%0 Journal Article
%T Recognizing Realistic Human Actions Using Accumulative Edge Image<br>基于累积边缘图像的现实人体动作识别
%A CHEN Xian-Gan
%A LIU Juan
%A GAO Zhi-Yong
%A LIU Hai-Hua
%A <br>谌先敢
%A 刘娟
%A 高智勇
%A 刘海华
%J 自动化学报
%D 2012
%I 
%X The problem of extracting feature from unconstrained videos for representing human actions has been investigated in order to recognize human actions in complex environment in this paper. Firstly, morphological gradient was used to eliminate most background information. Then, edge of shape was extracted and accumulated to a frame, which was named accumulative edge image (AEI). Grid-based histograms of orientation gradients (HOG) were calculated and formed a feature vector that captured the characteristic of human actions in this video sequence. Using support vector machine (SVM), the method was tested on the YouTube action dataset. The obtained impressive results showed that this method was more effective than other methods in YouTube action dataset.
%K Action recognition
%K accumulative edge image (AEI)
%K histograms of orientation gradients (HOG)
%K support vector machine (SVM)<br>动作识别
%K 累积边缘图像
%K 方向梯度直方图
%K 支持向量机
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=21CB0722CA48644F901A56907126679C&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=5D311CA918CA9A03&sid=41685CA5511D97F7&eid=7A60741D2B519BE0&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=19