DocumentCode :
3209838
Title :
Detecting unusual activity in video
Author :
Zhong, Hua ; Shi, Jianbo ; Visontai, Mirkó
Author_Institution :
Dept. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume :
2
fYear :
2004
fDate :
27 June-2 July 2004
Abstract :
We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selections are used. We divide the video into equal length segments and classify the extracted features into prototypes, from which a prototype-segment co-occurrence matrix is computed. Motivated by a similar problem in document-keyword analysis, we seek a correspondence relationship between prototypes and video segments which satisfies the transitive closure constraint. We show that an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space. We prove that an efficient, globally optimal algorithm exists for the co-embedding problem. Experiments on various real-life videos have validated our approach.
Keywords :
feature extraction; matrix algebra; surveillance; N-D Euclidean space; coembedding problem; complex activity models; document-keyword analysis; feature extraction; globally optimal algorithm; large video set; prototype-segment cooccurrence matrix; supervised feature selections; transitive closure constraint; unusual activity detection; Computer science; Event detection; Feature extraction; Hidden Markov models; Image segmentation; Information science; Layout; Object detection; Prototypes; Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2158-4
Type :
conf
DOI :
10.1109/CVPR.2004.1315249
Filename :
1315249
Link To Document :
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