Home Surveillance System Using Internet of Things
01 Jan 2018-pp 79-85
TL;DR: The implementation of motion detection algorithm for live camera streaming is presented and thus allows analyzing incoming image stream, and recognizing any movement occurs in the area and if so then triggers video recorder to save the information-contained video clip.
Abstract: In recent years, lots of research have been done in computer vision domain. Video surveillance in real-time scenario, especially for humans, like tracking and behavior analysis is one of the most active research topics in computer vision and artificial intelligence in present situation. The main focus is providing the low-cost and efficient video surveillance system for home application and can have wide scope in other areas such as elevator monitoring and server room monitoring. As the traditional surveillance system requires huge storage capacity and consumes lot of network bandwidth, hence it becomes necessary to provide solution for such design issues. This paper presents the implementation of motion detection algorithm for live camera streaming and thus allows analyzing incoming image stream, and recognizing any movement occurs in the area and if so then triggers video recorder to save the information-contained video clip.
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TL;DR: In this paper, a self-adapting, automatic updating background model is trained to adapt the slow and slight changes of the environment, which can also be applied for external environmental video surveillance.
Abstract: Intelligent video surveillance systems deals with the monitoring of the real-time environment. It monitors the transient and persistent objects within a specific environment. This is not only designed for security systems and can also be applied for external environmental video surveillance. The basic background subtraction algorithm is used for the detection of moving object. A self-adapting, automatic updating background model is trained to adapt the slow and slight changes of the environment. The foreground object is detected when the subtraction of the current image and the background, which is already trained attains a threshold, the foreground moving object is considered as to be in current view. The mobile phone automatically notifies the control unit through SMS(Short Message Service)or by phone call. Background subtraction technique basically works by feature analysis, pixel differences and so on. Here for feature analysis k-nearest neighbour algorithm (k-nn) is implemented. This proposed system requires little memory and less storage space than the previous. This can be used in implementing mobile based security monitoring system.
3 citations
30 May 2020
TL;DR: The BGS method was employed with fewer complications so that the approach can be utilised in a pragmatic manner where the real-time video surveillance systems (VSS) is highly needed.
Abstract: This paper illustrates the deployment of the background subtraction (BGS) approach in detecting and tracking the targeted moving objects (MOs). Through the BGS method, there is a potential of cost-saving because the process of storing data occurs once the motion is detected. The aim was to detect the MOs effectively. The applied technique is applicable at all scenarios and places that need the real-time video surveillance systems (VSS), including airports, forest, frequent entrances for criminals, traffic monitoring, country borders, cash machines, schools, banks, among other challenging outdoor and indoor areas. The concept of installing VSS is substantially much needed. The BGS method was employed with fewer complications so that the approach can be utilised in a pragmatic manner (conditions) where the VSS is highly needed. The VSS must be more convenient, effective and efficient to enhance advanced security systems. © 2020 Published by Faculty of Engineeringg
1 citations