Abnormal Behavior Detection by Multi-SVM-Based Bayesian Network

Chen, Yufeng; Liang, Guoyuan; Lee, Ka Keung; Xu, Yangsheng

doi:10.1109/icia.2007.4295746

Cited by 41 publications

(18 citation statements)

References 19 publications

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“…Its applications include surveillance [1], robot control [2], human computer interaction [3], and healthcare [4]. Generally, human motions during known actions are represented via extracted features, and then recognition of a new instance is performed by comparing and classifying them using these representations.…”

Section: Introductionmentioning

confidence: 99%

Joint movement similarities for robust 3D action recognition using skeletal data

Pazhoumand-Dar

Lam

Mašek

2015

Journal of Visual Communication and Image Representation

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Section: Introductionmentioning

confidence: 99%

Joint movement similarities for robust 3D action recognition using skeletal data

Pazhoumand-Dar

Lam

Mašek

2015

Journal of Visual Communication and Image Representation

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“…Apart from these, the automatic alarm management in case of suspicious activity, which is an important part of an automatic surveillance system. Automatic understanding of abnormal activity or anomaly detection is another research challenge in intelligent visual surveillance [47][48][49][50][51][52][53][54]. Because, many anomalydetection algorithms deal with highly structured scenes.…”

Section: Development Of Viewpoint Invariance Methodmentioning

confidence: 99%

Exploring object segmentation methods in visual surveillance for human activity recognition

Vaniya

Bharathi

2016

2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)

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In recent years, human activity recognition in intelligent visual surveillance has drawn much attention in the field of video analysis technology due to the growing demand from many applications, such as security and surveillance, sports and gaming, healthcare, person identification at distance, crowd behavior analysis, suspicious event detection and alarming in public/private places, traffic management, etc. Generally speaking, the human activity recognition in visual surveillance divided into following stages: object (human or vehicle) segmentation, feature extraction, object classification, object tracking, and activity recognition. Development of robust object segmentation method is the prime objective for any visual surveillance system. Object segmentation is used to detect the regions corresponding to static or moving human or vehicle. In this paper, we provide a comprehensive survey of the recent development of object segmentation (especially on human) algorithms in the context of human activity recognition in visualsurveillance. We will also discuss the strength and weakness of algorithms, complexities in activity understanding and identify the possible future research challenges.

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“…In Ref. [6], a learning method based on the multiple support vector machines was used to learn the selected action samples. Then the action could be classified to be normal or abnormal.…”

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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Abnormal Behavior Detection by Multi-SVM-Based Bayesian Network

Cited by 41 publications

References 19 publications

Joint movement similarities for robust 3D action recognition using skeletal data

Joint movement similarities for robust 3D action recognition using skeletal data

Exploring object segmentation methods in visual surveillance for human activity recognition

Unusual event detection and prediction in real-life scenes

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