Low cost smart security camera with night vision capability using Raspberry Pi and OpenCV

Abaya, Wilson Feipeng; Basa, Jimmy; Sy, Michael; Abad, Alexander C.; Dadios, Elmer P.

doi:10.1109/hnicem.2014.7016253

Cited by 66 publications

(16 citation statements)

References 1 publication

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“…Recently, in [40,41], authors proposed the process execution of object classification based on Haar-features with the same platform. We adopt LBP descriptors and Adaboost classification, which is distinct from previous published studies to achieve better running time.…”

Section: Resultsmentioning

confidence: 99%

“…When the user walks faster, the accuracy decreases due to calculation error of homography matrix. [40,41] 0.556 LBP descriptors 0.1969 With the same speed but different environments, the accuracy is different, due to the vibrational background. In indoor environments, we get more stable key points from the non-moving objects such as walls and doors.…”

Section: Resultsmentioning

confidence: 99%

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Implementation of Noise Reduction Methods for Rear-View Monitoring Wearable Devices

Nguyễn

Kim

et al. 2016

JSN

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This study suggests effective noise reduction methods for wearable neckband devices, which are able to monitor users' rear-view areas. The wearable neckband device helps the user to monitor rear-view areas in which he/she is unable to see in normal ways (without turning back). Unlike general computers or supercomputer systems, the neckband devices have some particular constraints such as small size, lightweight and low power consumption. In a general vision system, there are many kinds of noises, which significantly decrease system quality such as impulse noise, random noise, motion noise, etc. These noises also affect wearable devices, which use cameras as the system input. Moreover, when the user walks or runs, the neckband device moves accordingly. The changing position of the neckband device causes many other noises such as camera motion (ego-motion) noises. Furthermore, when the user walks from indoors to outdoors or vice versa, the illumination dramatically changes, which also affects the device performance. Effective noise reduction methods to deal with these noises are proposed in this study. Random noise and other small noises are removed by using a Gaussian filter

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Implementation of Noise Reduction Methods for Rear-View Monitoring Wearable Devices

Nguyễn

Kim

et al. 2016

JSN

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“…The next important task is to sense the environmental conditions which include temperature, humidity gas, smoke etc. the readings obtained from these sensors will help the controller to analyze the situation in the Warfield and forward the soldiers towards the Warfield with precautionary measures [15].The next important activity is to find the enemy soldiers in the Warfield and the number of them who are present there hence the required tactics to manage with them and prior decisions can be made from this information. Hence the controller present at a certain distance can aggregate the information obtained from various sources and guide the soldiers accordingly.…”

Section: A Robotic Movement Controlmentioning

confidence: 99%

Raspberry Pibot Manipulated with Skid-Steered Mechanism for Multi-Environment Surveillance

V¹,

K²,

P³

2018

IJTSRD

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“…The current CCTV system is costly and not suitable for normal resident [1]. Although surveillance camera records video and helps the authorities to identify the cause of an incident such as crime or accident, it is just a passive monitoring device [2]. Most of the CCTV systems in the market provide a system that can only view the current image, and if the system is equipped with alarm system, the sirens will be activated once the intruders get in into the house.…”

Section: Introductionmentioning

confidence: 99%

Smart Surveillance System using Background Subtraction Technique in IoT Application

Harum¹,

Ali²,

Zakaria³

et al. 2018

ijacsa

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This paper presents a development of a security system based on Internet-of-Things (IoT) technology, where an IoT device, Raspberry Pi has been used. In the developed surveillance system, a camera works as a sensor to detect motion, and automatically capture the video of the view of area where the motion is detected. The motion is detected by image processing techniques; background subtraction technique. The technique is applied by comparing two different captured images using Pi NoIR camera. The system can be controlled from anywhere using Telegram application, and users will receive alert message with video using the application. The user can also play a siren from anywhere once detecting suspicious object can access images and videos using Telegram application. This can frighten the thief if the crime is suspected in home or office. Users can also deactivate and activate the system from anywhere at any time using the Telegram. The functionality tests have been done to ensure the developed product can work properly. Besides, tests to identify a suitable video length to be transmitted to the user and to identify the adequate location of the security in order to minimize false detection as well as false alert have been performed. The project is an IoT-based which significantly in line with the Industrial Revolution 4.0, supporting the infrastructure of Cyber-Physical System.

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Low cost smart security camera with night vision capability using Raspberry Pi and OpenCV

Cited by 66 publications

References 1 publication

Implementation of Noise Reduction Methods for Rear-View Monitoring Wearable Devices

Implementation of Noise Reduction Methods for Rear-View Monitoring Wearable Devices

Raspberry Pibot Manipulated with Skid-Steered Mechanism for Multi-Environment Surveillance

Smart Surveillance System using Background Subtraction Technique in IoT Application

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