SDN-MCHO: Software Define network based Multi-criterion Hysteresis Optimization based for reliable device routing in Internet of Things for the smart surveillance application

With the evolution of technology ensuring people for their safety and security all around the time constantly is a big challenge. We propose an advanced technique based on deep learning and artificial intelligence platform that can monitor the people, their homes and their surroundings providing them a quantifiable increase in security. We have surveillance cameras in our homes for video capture as well as security purposes. Our proposed technique is to detect and classify as well as inform the user if there is any breach in security of the classified object using the cameras by implementing deep learning techniques and the technology of internet of things. It can serve as a perimeter monitoring and intruder alert system in smart surveillance environment. This paper provides a well-defined structure for live stream data analysis. It overcomes the challenge of static closed circuit cameras television as it serves as a motion based tracking system and monitors events in real time to ensure activities are limited to specific persons within authorized areas. It has the advantage of creating multiple bounding boxes to track down the objects which could be any living or non-living thing based on the trained modules. The trespasser or intruder can be efficiently detected using the CCTV camera surveillance which is being supported by the real-time object classifier algorithm at the intermediate module. The proposed method is mainly supported by the real time object detection and classification which is implemented using Mobile Net and Single shot detector.

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Fast coding unit (CU) determination algorithm for high-efficiency video coding (HEVC) in smart surveillance application

The Journal of Supercomputing ◽

10.1007/s11227-016-1730-y ◽

2016 ◽

Vol 73 (3) ◽

pp. 1063-1084 ◽

Cited By ~ 8

Author(s):

Byung-Gyu Kim

Keyword(s):

Video Coding ◽

High Efficiency ◽

High Efficiency Video Coding ◽

Smart Surveillance ◽

Coding Unit ◽

Surveillance Application

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Learning-Aided IoT Set-Up for Home Surveillance Applications

Predictive Intelligence Using Big Data and the Internet of Things - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-6210-8.ch008 ◽

2019 ◽

pp. 180-205

Author(s):

Jutika Borah ◽

Kandarpa Kumar Sarma ◽

Pulak Jyoti Gohain

Keyword(s):

Decision Support ◽

Internet Of Things ◽

Support System ◽

Surveillance Systems ◽

Human Intervention ◽

Automated Systems ◽

Wireless Devices ◽

Set Up ◽

Surveillance Application ◽

Surveillance Applications

Of late, home surveillance systems have been enhanced considerably by resorting to increased use of automated systems. The automation aspect has reduced human intervention and made such systems reliable and efficient. With the proliferation of wireless devices, networking among the connected devices is leading to the formation of internet of things (IoT). This has made it essential that home surveillance systems be also automate using IoT. The decision support system (DSS) in such platforms necessitates that automation be extensive. It necessitates the use of learning-aided systems. This chapter reports the design of IoT-driven learning-aided system for home surveillance application.

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