Implementation of IoT-Based Smart Video Surveillance System
01 Jan 2017-pp 771-780
TL;DR: The proposed system intimates about the presence of any person in the premises, also providing more security by recording the activity of that person, and is intelligent enough to optimize power consumption wastage if user forgets to switch off any electronic appliances by customizing coding with specific appliances.
Abstract: Smart video surveillance is a IOT-based application as it uses Internet for various purposes. The proposed system intimates about the presence of any person in the premises, also providing more security by recording the activity of that person. While leaving the premises, user activates the system by entering password. System working starts with detection of motion refining to human detection followed by counting human in the room and human presence also gets notified to neighbor by turning on alarm. In addition, notification about the same is send to user through SMS and e-mail. The proposed system’s hardware implementation is supported by Raspberry Pi and Arduino board; on the other hand, software is given by OpenCV (for video surveillance) and GSM module (for SMS alert and e-mail notification). Apart from security aspect, system is intelligent enough to optimize power consumption wastage if user forgets to switch off any electronic appliances by customizing coding with specific appliances.
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10 Sep 2020
TL;DR: A study on the background, state-of-the-art, growth, key players, applications, challenges, and future opportunities in the area of Internet of Things (IoT) in the Kingdom of Saudi Arabia.
Abstract: The role of the Internet has significantly changed due to the development in communication technologies. Nowadays, billions of people and physical devices are connected via Internet. In near future, storage and computational services will be more pervasive and distributed. Even in recent times, we can see that people, machines, objects, and platforms are connected with wireless or wired sensors. Considering such an internet setting with billions of connected devices, in this paper, we present a study on the background, state-of-the-art, growth, key players, applications, challenges, and future opportunities in the area of Internet of Things (IoT). The Kingdom of Saudi Arabia’s IoT and M2M (Machine to Machine) communication market is estimated to grow to $16.01 billion by 2019 from $4.88 billion in 2014[26]. We also discuss general aspects and issues of IoT and explore the implication of all these in a developing country’s setting taking the case of Saudi Arabia.
8 citations
Cites methods from "Implementation of IoT-Based Smart V..."
...Smart surveillance [6], automated transportation, children tracking[5], smarter energy management systems [7, 8], water distribution, urban security and environmental monitoring, smart garbage/waste management using CrAN (Crowd Associated Network) [4], Green and clean environment, all are examples of Internet of Things applications for smart cities....
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01 Dec 2017
TL;DR: A hybrid classification approach for human behavior identification employing support vector machines (SVMs) classifier hybrid with Elephant Herding Optimization algorithm (EHO) with superiority over other techniques on the same data set regarding classification accuracy.
Abstract: Human behavior identification has a great importance in daily life. Security, gaming, and medical diagnosis are vital applications in that field. This paper introduces a hybrid classification approach for human behavior identification employing support vector machines (SVMs) classifier hybrid with Elephant Herding Optimization algorithm (EHO). The Elephant Herding Optimization algorithm used to fine-tune SVM parameters and to select most discriminant features. Validation of the proposed approach will be accomplished using a computer vision-based data set named Vicon. It was acquired from multiple human action detection experiments. Results show superiority for the proposed approach over other techniques on the same data set regarding classification accuracy. EHO-SVM hybrid algorithm reaches 91.21 % and 90.62 % accuracies for two test cases with different action class selections.
5 citations
10 Jun 2019
TL;DR: A high-performance STM32F7 microcontroller is used as a main digital signal processor (DSP) to acquire and transfer images at full rate, keeping the core idle to do real-time image processing.
Abstract: This paper presents a new proposal of a low-cost and low-power wireless smart camera device, designed to be used as an
inexpensive smart image acquisition edge node for Internet of Things (IoT). Taking the advantages of the ARM CortexM7 microcontrollers, a high-performance STM32F7 microcontroller is used as a main digital signal processor (DSP) to
acquire and transfer images at full rate, keeping the core idle to do real-time image processing. In order to design a very
compact, low-cost and low-power wireless image sensor node, the DSP is supported by an embedded 1.3 Mega Pixel
CMOS camera sensor and a low consumption 2.4GHz Wi-Fi module. This setup allows a custom build-in image
processing algorithm for specific IoT sensing applications or a simply setup as low-cost streaming Motion JPEG (MJPEG)
wireless camera node. In this work, both configurations have been tested and analyzed. Results show that the device can
acquire and process images simultaneously at a full rate (30 fps) and the MJPEG transmission reaches 7.23 fps with a
QVGA resolution.
4 citations
Cites methods from "Implementation of IoT-Based Smart V..."
...[14] present a smart video surveillance system for IoT, supported by a Raspberry Pi and a USB camera using...
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01 Jan 2021
TL;DR: In this paper, a real-time object detection and alert system (ODAS) is proposed, which uses the YOLO framework to detect objects within images and live videos.
Abstract: In recent years, real-time surveillance and monitoring system is getting importance because of security reasons. Government organizations, residential areas, commercial complexes, schools and colleges, industries, borders, and others require a dedicated surveillance system. The traditional surveillance systems do not provide real-time object identification and alerts. This paper aims to design and develop a real-time object detection and alert system (ODAS). The proposed system could be used as a surveillance and monitoring system. The object detection system uses the YOLO framework to detect objects within images and live videos. Object identity and time stamp, along with bounding box image, are marked by the detection system and stored locally. Also, the collected information is transmitted to the server in real time. At the server, the graphical user interface (GUI) application continuously gathers information from different nodes, analyzes it, and fires an alert message if the anomalies/targeted object is found. GUI also facilitates us to analyze the obtained information of an individual node or in the combination that infers the direction of movement made by a detected object and its current position. ODAS also takes the services of the real-time database to provide real-time updates of anomalies on the android phone/tablet.
2 citations
TL;DR: A situation assessment model, which evaluates the security risk of video transmission network information from four aspects: front-end perception layer, transmission layer, application layer and other risks, is proposed.
Abstract: In this paper, we propose a situation assessment model for video transmission network, which evaluates the security risk of video transmission network information from four aspects: front-end perception layer, transmission layer, application layer and other risks.We divide the video network security evaluation system into three layers, and use AHP to determine the weight of each index.The weights of these four aspects are calculated by analytic hierarchy process.The method proposed in this paper can provide an evaluation system and a calculation method for the safe operation of video transmission network and important systems, and can provide specialized information security services for the construction project of video transmission network.
1 citations
Cites background from "Implementation of IoT-Based Smart V..."
...However, threat intelligence sharing in dynamic, open and diversified video surveillance network space has the characteristics of massive data, low value information density and ambiguous quality[18] ....
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79 citations
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