Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems

Duque, Duarte; Santos, Henrique; Cortez, Paulo

doi:10.1109/cidm.2007.368897

Cited by 64 publications

(22 citation statements)

References 9 publications

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“…Duque et. al [3] developed a video surveillance system named OBSERVER that detected and predicted abnormal behaviors aiming at the intelligent surveillance concept. In the paper, a method called Dynamic Oriented Graph (DOG) was used to detect and predict abnormal behaviors, via real-time unsupervised learning method.…”

Section: A Related Workmentioning

confidence: 99%

HMMs-based human action recognition for an intelligent household surveillance robot

Zhou

Wang

et al. 2009

2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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The aging of population has become a social problem and fall is a major health risk in the elderly. To this end, this paper presents a novel approach for fall detection applied to an intelligent household surveillance robot. Silhouette based features are extracted, including aspect ratio of minimal bounding box of the human silhouette, approximated elliptical eccentricity, normalized central moments and Hu moments. Fall and other human motions, such as walk, bend, run and crouch, are modeled using Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM). The experimental results are evaluated by sensitivity, specificity and accuracy and the average of them reaches 88.71%, 97.56% and 96.26% respectively.

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Section: A Related Workmentioning

confidence: 99%

HMMs-based human action recognition for an intelligent household surveillance robot

Zhou

Wang

et al. 2009

2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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“…A co-occurrence statistic is accumulated over the codebook and a hierarchical classification performed to identify normal and abnormal activities. In [23] a Dynamic Oriented Graph (DOG) is used to structure common patterns of objects activities. The entrance, path and departure nodes are used to construct the graph.…”

Section: Related Workmentioning

confidence: 99%

Human behavioural analysis with self-organizing map for ambient assisted living

Appiah

Hunter

Lotfi

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints.

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“…The events from all these technologies are indexed cross indexed into a common repository allowing for correlation across multiple sensors and event types. Duque et al [3] presented an abnormal behavior analysis system. A novel method, called dynamic oriented graph (DOG) is used to detect and predict abnormal behaviors.…”

Section: Introductionmentioning

confidence: 99%

Unusual event detection and prediction in real-life scenes

Zhang

Yang

2010

J. Shanghai Jiaotong Univ. (Sci.)

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In this paper, we consider unusual event detection problem in a novel viewpoint and provide an algorithm to solve the problem. The actions or events in the scene is usual or not will eventually be reflected on the changes of some basic features. We summarize these basic event features and propose special representation for each of them. Thus we can model these features in a uniform mode using adaptive Gaussian mixture model. Supervised and unsupervised unusual event detection algorithm can be designed to fit various situations based on this model. The superiority of our model is that it can detect unusual event automatically without to know the determinate model of unusual events. In conclusion, we provide two applications to verify the effectiveness of our model.

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Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems

Cited by 64 publications

References 9 publications

HMMs-based human action recognition for an intelligent household surveillance robot

HMMs-based human action recognition for an intelligent household surveillance robot

Human behavioural analysis with self-organizing map for ambient assisted living

Unusual event detection and prediction in real-life scenes

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