Machine learning based Smart Surveillance System

2019 ◽  
Author(s):  
Vanita Babanne ◽  
Nikita Sandeep Mahajan ◽  
Renu Lalchand Sharma ◽  
Pratiksha Pradip Gargate
Keyword(s):  
Machine Learning ◽  
Surveillance System ◽  
Smart Surveillance ◽  
Smart Surveillance System

scholarly journals A Smart Survillience System

2020 ◽  
Vol 9 (1) ◽  
pp. 1135-1138
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Efficient Method ◽  
Surveillance System ◽  
Machine Learning Algorithms ◽  
Surveillance Video ◽  
Security Systems ◽  
Smart Surveillance ◽  
Human Faces ◽  
Smart Surveillance System

The smart surveillance system defines a approach to identify and recognize human faces from the surveillance videos. It is very tedious to find particular person within a video. This system gives a quick and efficient method to find the presence of a person within a surveillance video. The smart surveillance system uses various machine learning algorithms like Face Recognition and Face Detection to achieve the required results. This system can be used in many security systems to check the presence of a person in any video of the particular area.

scholarly journals Remote Fault-Tolerant Control for Industrial Smart Surveillance System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Atif Mahmood ◽  
Abdul Qayyum Khan ◽  
Ghulam Mustafa ◽  
Nasim Ullah ◽  
Muhammad Abid ◽  
...  
Keyword(s):  
Surveillance System ◽  
Fault Tolerant ◽  
Sufficient Conditions ◽  
Fault Tolerant Control ◽  
Linear Matrix ◽  
Control Law ◽  
Uncertain Network ◽  
Smart Surveillance ◽  
Communication Links ◽  
Smart Surveillance System

We design a remote fault-tolerant control for an industrial surveillance system. The designed controller simultaneously tolerates the effects of local faults of a node, the propagated undesired effects of neighboring connected nodes, and the effects of network-induced uncertainties from a remote location. The uncertain network-induced time delays of communication links from the sensor to the controller and from the controller to the actuator are modeled using two separate Markov chains and packet dropouts using the Bernoulli process. Based on linear matrix inequalities, we derive sufficient conditions for output feedback-based control law, such that the controller does not directly depend on output, for stochastic stability of the system. The simulation study shows the effectiveness of the proposed approach.

scholarly journals IoT Based Smart Surveillance System for Healthcare Monitoring using Raspberry PI

2019 ◽  
Vol 8 (12) ◽  
pp. 627-629
Keyword(s):  
Blood Pressure ◽  
Surveillance System ◽  
Heart Diseases ◽  
Electrical Signal ◽  
Raspberry Pi ◽  
Electronic Board ◽  
Pulse Sensor ◽  
Smart Surveillance ◽  
Healthcare Monitoring ◽  
Smart Surveillance System

IOT could be a trending in technology that can transform any device into a wise one a lot of industries setting out to utilize these technologies to extend their capacity and improve potency. These system has been created to detect people who are suffering with heart diseases, this framework is powered by Raspberry pi electronic board, which is worked on power control supply, Remote web availability by utilizing USB modem, it incorporates with sensors. pulse sensor which detects each beats per minute price. Temperature sensor detects the temperature variation, blood pressure sensor reads blood pressure and heart rate, ECG sensor which measures the electrical signal of the heart. it is an analog from converted in digital by using of SPI protocol. If any emergency occurs, it will raise a caution send it to the website and mobile though NOOBS Software. If any sensor parameter value more than the instructed value it will raise a beep sound

Real-Time Smart Surveillance System Using Raspberry Pi

2019 ◽  
Author(s):  
Dhanshri Mali ◽  
Ramesh RTP ◽  
Nagaraj Dharwadkar ◽  
Chaitanya R. Devale ◽  
Omprakash Tembhurne
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Smart Surveillance ◽  
Smart Surveillance System
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