Next: Introduction
Detecting Unusual Activity in Video
Hua Zhong | | |
Computer Science Department | | Computer and Information Science |
Carnegie Mellon University | | University of Pennsylvania |
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.
Mirko Visontai
2004-05-13