Corpus ID: 14669493

Bayesian Network based Abnormality Detection with Genetic Algorithm optimization

  title={Bayesian Network based Abnormality Detection with Genetic Algorithm optimization},
  author={Jingbang Qiu and Chongyang Zhang and Shibao Zheng},
  journal={International Conference on Computational Problem-Solving},
Abnormality Detection (AD), being the core part of intelligent surveillance systems, is calling for growing research interest due to its importance in providing higher efficiency and labor saving. In this paper, we propose a novel Bayesian Network (BN) based AD method for smart surveillance in scenes containing large scale viewpoint changes without model-relearning. In the proposed AD scheme, Reasoning Layer is introduced into BN to strengthen logical inferences, and a localized Genetic… Expand
Learning bayesian network by genetic algorithm using structure-parameter restrictions
In this paper, a novel Bayesian Network (BN) learning method is proposed, in which Genetic Algorithm (GA)and structure-parameter restrictions are combined to optimize the BN's structure andExpand
Cross-Layered Hidden Markov Modeling for Surveillance Event Recognition
Experimental results on typical surveillance test sequences showed that CLHMM outperforms LHMM in terms of accuracy and computational complexity. Expand


Unsupervised Abnormality Detection in Video Surveillance
It is shown that the additive property of CHLAC in combination with a linear subspace method is well suited to simplify the learning of normal movements and to detect abnormal movements even in scenes containing multiple persons. Expand
Bayesian framework for video surveillance application
The goal of this paper is to describe and demonstrate the application of Bayesian networks in a generic automatic video surveillance system and to demonstrate the effectiveness of the approach by training the networks with 600 image frames belonging to one domain of interest and applying them to image sequences in a different domain. Expand
A Bayesian approach to human activity recognition
  • A. Madabhushi, J. Aggarwal
  • Sociology
  • Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223)
  • 1999
Presents a methodology for automatically identifying human action. We use a new approach to human activity recognition that incorporates a Bayesian framework. By tracking the movement of the head ofExpand
Learning and inferring transportation routines
A hierarchical Markov model that can learn and infer a user's daily movements through an urban community and an application called ''Opportunity Knocks'' that employs the techniques to help cognitively-impaired people use public transportation safely. Expand
HMM-Based Segmentation and Recognition of Human Activities from Video Sequences
A novel HMM-based approach that uses threshold and voting to automatically and effectively segment and recognize complex activities that are composed of more than one single activity is presented. Expand
Genetic Algorithms in Search Optimization and Machine Learning
This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Expand
Statistical Cognition Theory and Its Applications:An Overview
This paper will give a brief overview of statistical cognition as it stands today, and discuss some future research. Expand
Coupled hidden Markov models for complex action recognition
  • M. Brand, N. Oliver, A. Pentland
  • Computer Science
  • Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1997
We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifyingExpand
Independent Viewpoint Silhouette-Based Human Action Modeling and Recognition
This paper addresses the problem of silhouette-based human action modelling and recognition independently of the camera point of view by comparing a 2D motion plate, built from observations, with learned models of the same type from a wide range of viewpoints. Expand
Recognition and interpretation of parametric gesture
The approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the states of the HMM. Expand