Practical design and implementation of recognition assisted dynamic surveillance system

Leu, Jenq‐Shiou; Tzeng, Hung-Jie; Chen, Chi-Feng; Lin, Wei‐Hsiang

doi:10.1016/j.compeleceng.2011.05.015

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Video detecting category addresses issues regarding to automatic detection of anomalous, forbidden, dangerous events or abandoned object (counting moving people, ship detection, after-the-fact event, intruder detection, trajectory-based unusual behavior detection, motion detection, mult iple moving object detection, face detection, pedestrian detection, vehicle detection, unattended object detection, etc) . Video encoding [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [16], [104] [105], [106], [107], [108], [109], [110], [7], [111] [112], [113], [114], [115] [116],[8], [117] Object detection is performed by co mmon statistical learning techniques with dynamically learning background model of the scene and applies the reference model to find out which section of the scene match with mov ing object. Reasoning refers to generating new explanations, facts and knowledge of dynamic scenes by applying inference engine and method (rule and case based reasoning, Bayesian network, decision tree).…”

Section: Communication Layermentioning

confidence: 99%

Systematic Review and Classification on Video Surveillance Systems

Chamasemani¹,

Affendey

2013

IJITCS

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Abstract-Recently, various conferences and journals have published articles related to Video Surveillance Systems, indicating researchers' attention. The goal of this review is to examine the latest works were published in journals, propose a new classification framework of video surveillance systems and investigate each aspect of this classification framework. This paper provides a co mprehensive and systematic literature review of v ideo surveillance systems from 2010-2011, extracted fro m six online d igital libraries using article's t itle and keyword. The proposed classification framework is expanded on the basis of architecture of v ideo surveillance systems, which is composed of six layers: Concept and Foundation Layer, Network Infrastructure Layer, Processing Layer, Co mmunicat ion Layer, Applicat ion Layer, and User Interaction Layer. Th is review shows , although many publication and research focus on real-time aspect of the challenge, only few researches have investigated the deployment of ext racted and retrieved information for forensic video surveillance.

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