DocumentCode
2737680
Title
Video data mining based on K-Means algorithm for surveillance video
Author
Wang, Jinghua ; Zhang, Guoyan
Author_Institution
Comput. Sci. & Technol. Dept., Hua Zhong Normal Univ., Wu Han, China
fYear
2011
fDate
21-23 Oct. 2011
Firstpage
623
Lastpage
626
Abstract
In this paper, we propose a new data mining algorithm, which is used in surveillance video of stationary places. The algorithm combines Background Subtraction with Symmetrical Differencing in order to extract moving targets. According to the amount of motions occurring in video frames, we divide the video into different segments. Video segments are clustered via the improved K-Means algorithm. Then we find the abnormal events, congestions and similar situation retrieval effectively in this way. To a certain extent, intelligent surveillance is implemented well.
Keywords
data mining; feature extraction; pattern clustering; video surveillance; background subtraction; intelligent surveillance; k-means algorithm; moving target extraction; surveillance video; symmetrical differencing; video data mining; video frames; video segments; Classification algorithms; Clustering algorithms; Data mining; Motion segmentation; Silicon; Streaming media; Surveillance; K-means; surveillance video mining; video mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Analysis and Signal Processing (IASP), 2011 International Conference on
Conference_Location
Hubei
Print_ISBN
978-1-61284-879-2
Type
conf
DOI
10.1109/IASP.2011.6109120
Filename
6109120
Link To Document