Detection of Abnormal Human Behavior Using a Matrix Approximation-Based Approach
Authors:
- Lijun WangLWLijun Wang
- Ming DongMDMing Dong
- 1 December 2014
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 324-329
Abstract
Automatic detection of abnormal events is one of central tasks in video surveillance. In this paper we present a matrix approximation-based method to detect abnormal human behavior. In our model, a behavior pattern is represented by a motion matrix obtained through object tracking. We model typical motions associated with normal behaviors with a set of motion subspaces, computed through low-rank matrix approximation. Then, abnormal human behaviors are identified by the motion deviations from the representative subspaces. Our method does not require a complicated classification procedure, and can fast detect abnormal events in complex scenes. In addition, through the adaptive learning module, our model is built on the observed data, and can be expanded by incorporating new behavior patterns during the detection process. The results on simulated surveillance videos show the effectiveness of our method.Keywords
This publication has 22 references indexed in Scilit:
- Multi-level Low-rank Approximation-based Spectral Clustering for image segmentationPattern Recognition Letters, 2012
- Abnormality detection using low-level co-occurring eventsPattern Recognition Letters, 2011
- Local Abnormality Detection in Video Using Subspace Learning2010
- Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models2009
- Colibri2008
- Tensor reduction error analysis — Applications to video compression and classification2008
- Less is More: Sparse Graph Mining with Compact Matrix DecompositionStatistical Analysis and Data Mining: An ASA Data Science Journal, 2007
- Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix DecompositionSIAM Journal on Computing, 2006
- Algorithm 844ACM Transactions on Mathematical Software, 2005
- Spectral grouping using the nystrom methodIEEE Transactions on Pattern Analysis and Machine Intelligence, 2004