REAL-TIME ANOMALY DETECTION THROUGH INTELLIGENT VIDEO ANALYTICS.
Surveillance camera is one of the foremost tool for monitoring movements of human and for preventing unwanted and unintended activities. Video Surveillance demands crime and avert unfortunate consequences which impacts human society. Convolutional neural network has shown promising future object det...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2087 - 2096 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006202&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006202 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006202 151006202 151006202 151006202 ppf: 2087 ppct: 9 formats: fmt: @attributes: type: P tig: atl: REAL-TIME ANOMALY DETECTION THROUGH INTELLIGENT VIDEO ANALYTICS. aug: au: MOHANRAJ, S. DHARANIYA, R. HARISHMA, I. RUBALAXMI, A. affil: Assistant Professor, Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India sug: subj: Videorecording Evaluation Spatial Perception Security Measures Neural Networks (Computer) Benchmarking Security Measures Labor Supply Motivation Human Error Probability Image Processing, Computer Assisted Classification Information Retrieval Data Management ab: Surveillance camera is one of the foremost tool for monitoring movements of human and for preventing unwanted and unintended activities. Video Surveillance demands crime and avert unfortunate consequences which impacts human society. Convolutional neural network has shown promising future object detection and recognition, particularly in images and videos. However, labels are required for learning as convolutional neural network is a supervised technique. For anomaly detection in videos, we propose a spatiotemporal architecture which includes representation of spatial feature and evolution of spatial features. Benchmark confirms the accuracy of our proposal in comparison with state-of-the-art method. Hence, abnormal and potentially unsafe activities which are even hidden from a security personnel will be easily detected. The key motivation for development and deployment of our proposed method is to avoid existing human error while monitoring and to ensure security of a person or organization's security. It is well known that anomalies are highly contextual. For example, riding a bicycle in park or road would be normal but riding the bicycle in pedestrian path would be an anomaly. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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