CCTV-Based Surveillance System with Face Recognition Feature

Lumaban, Michael Bryan P.

doi:10.30534/ijatcse/2020/5491.32020

Cited by 4 publications

(2 citation statements)

References 8 publications

(9 reference statements)

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“…(Lumaban and Battung 2020) [8] The aim of this research paper is to develop a system to help detect the presence of criminals using descriptive and system development methods. For Software Rapid Application Development(RAD) methodology.…”

Section: Related Workmentioning

confidence: 99%

IoT Based Surveillance System Using DNN Model

Raj¹

2021

IJRASET

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Localization, Visibility, Proximity, Detection, Recognition has always been a challenge for surveillance system. These challenges can be felt in the industries where surveillance systems are used like armed forces, technical-agriculture and other such fields. Most of the Smart system available are just for the surveillance of Human intervention but there is a need for a system which can be used for animals as well because with the outburst of human population and symbiotic relationship with wild animals results in life loss and damage to agriculture. In this paper we are designing to overcome these above-mentioned challenges for human and animal-based surveillance system in real time application. The system setup is done on a Raspberry pi integrated with deep-learning models which performs the classification of objects on the frames, then the classified objects is given to a face detection model for further processing. The detected face is relayed to the back-end for feature mapping with the saved log files with containing features of familiar face IDs. Four models were tested for face detection out of which the DNN model performed the best giving an accuracy of 94.88%.The system is also able to send alerts to the admin if any threat is detected with the help of a communication module. Keywords: Deep learning, Raspberry Pi, OpenCV, Image Processing, YOLO, Face Recognition

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Section: Related Workmentioning

confidence: 99%

IoT Based Surveillance System Using DNN Model

Raj¹

2021

IJRASET

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“…Lopez et al [3] envisioned the relationship between human and computers through integration of edge computing as well as human-centred system design techniques. The experiment was successful and contributed to the verdict of possibilities in correlating the different network technologies [57][58][59][60][61][62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Conceptual Analytics on Integration of Network Technologies with Crowdsourcing Infrastructure

Sivachandiran¹,

Jagan²,

Mohammed³

2021

Atlantis Highlights in Computer Sciences

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Technology seeks a new dimension as collaboration of various methods and technologies in transmission of data. Crowdsourcing enables the availability of huge data over a variety of networks. It is important to understand the needs of the management and monitoring of such data over a long distance and among a huge population especially among a big crowd like that of temples and other public places. Hence integration of network technologies has to be considered for effective attainment of data transfer with high speed and better performance that includes latency. The paper conducts analytics of various collaborated network technologies that was utilised to handle huge data from a crowdsourced network. The network technologies considered for the reviews are Edge computing, Fog computing, IoT based sensors, mobile technologies and cloud-based platforms. After significant analysis of all the research works, it was identified that edge computing has been the chief network technology used to transmit crowdsourced data with speed and efficiency. Cloud platforms assist the transferred data to be stored with privacy and security. The IoT sensors and Fog computing helped in detecting the data and also to increase the bandwidth and improve latency problems. The research gap identified was to enhance the speed and efficiency of transmission in public related problems like surveillance and handling public or social media database which seemed to be more challenging and encouraging future research works.

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CCTV-Based Surveillance System with Face Recognition Feature

Cited by 4 publications

References 8 publications

IoT Based Surveillance System Using DNN Model

IoT Based Surveillance System Using DNN Model

Conceptual Analytics on Integration of Network Technologies with Crowdsourcing Infrastructure

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