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Surveillance Drone

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u00a0Pranesh Dilip Tambe, Ameya Rajesh Pati, Kiran Dhaije, Ragini Sharma,

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nJanuary 9, 2023 at 5:38 am

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nAbstract

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In today’s society, surveillance is becoming increasingly important in order to maintain a place’s decorum and assure the safety and security of its inhabitants. In this case, an aerial monitoring system will be beneficial. This study explains how an unmanned aerial vehicle (drone) can be used to create an aerial surveillance system. We begin by describing the elements of our airborne surveillance system, followed by a discussion of some of the technologies we employed to construct it. Following that, we describe how we integrated those technologies into a drone and made them function in tandem to produce our intended aerial surveillance system. This system will be a practical and cost-effective replacement for present surveillance systems. It can be utilised for both peacekeeping and real-time monitoring of a location at any time of day. The goal is to create economical, rapid, and effective monitoring that may be employed broadly at the private, institutional, and governmental levels.

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Volume :u00a0u00a09 | Issue :u00a0u00a01 | Received :u00a0u00a0May 12, 2022 | Accepted :u00a0u00a0May 19, 2022 | Published :u00a0u00a0May 25, 2022n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Programming languages(rtpl)] [/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue Surveillance Drone under section in Recent Trends in Programming languages(rtpl)] [/if 424]
Keywords Drone, unnamed aerial vehicle (UAV), python programming, Raspberry Pi, OpenCV, android app

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References

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1. Ben Lutkevich, Earls Alan R. Drone (UAV). [Online]. TechTarget. Available from: https://www.techtarget.com/iotagenda/definition/drone.
2. Boon K, Lovelace DC, editors. The domestic use of unmanned aerial vehicles. Terrorism: Commentary on Security Documents. Vol. 134. Oxford University Press; 2014; 207–228.
3. Vitto V. Report of the defense science board task force on the investment strategy for darpa. Washington DC: Defence Science Board; 1999 Jul 1.
4. Matt Parker GB. Quadcopter. Fort Collins, Colorado-80523: Colorado State University; 2012.
5. Weiger RL. Military unmanned aircraft systems in support of homeland security. PA: Army War Coll Carlisle Barracks; 2007 Mar 30.
6. Williams BG. Predators: The CIA’s drone war on Al Qaeda. Potomac Books, Inc.; 2013 Jul 31.
7. Campbell JP. Vertical takeoff and landing aircraft. New York: The MacMillan Company; 1962.
8. Garrett Reim. (2019). DARPA cancels ARES cargo drone project with Lockheed Martin. [Online]. Flight Global. Available from: https://www.flightglobal.com/helicopters/darpa-cancels-ares-cargo- drone-project-with-lockheed-martin-/132645.article.
9. Cohen J. Drone spy plane helps fight California fires. Science. 2007; 318(5851): 727.
10. Roberts MR. 5 drone technologies for firefighting. Fire Chief Magazine; 2014.

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Editors Overview

rtpl maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

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    Pranesh Dilip Tambe, Ameya Rajesh Pati, Kiran Dhaije, Ragini Sharma

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  1. Student, Assistant Professor,Department of Information Technology, Saraswati College of Engineering, Kharghar Navi Mumbai, Department of Information Technology, Saraswati College of Engineering, Kharghar Navi Mumbai,Maharashtra, Maharashtra,India, India

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Abstract

nIn today’s society, surveillance is becoming increasingly important in order to maintain a place’s decorum and assure the safety and security of its inhabitants. In this case, an aerial monitoring system will be beneficial. This study explains how an unmanned aerial vehicle (drone) can be used to create an aerial surveillance system. We begin by describing the elements of our airborne surveillance system, followed by a discussion of some of the technologies we employed to construct it. Following that, we describe how we integrated those technologies into a drone and made them function in tandem to produce our intended aerial surveillance system. This system will be a practical and cost-effective replacement for present surveillance systems. It can be utilised for both peacekeeping and real-time monitoring of a location at any time of day. The goal is to create economical, rapid, and effective monitoring that may be employed broadly at the private, institutional, and governmental levels.n

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Keywords: Drone, unnamed aerial vehicle (UAV), python programming, Raspberry Pi, OpenCV, android app

n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Programming languages(rtpl)]

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1. Ben Lutkevich, Earls Alan R. Drone (UAV). [Online]. TechTarget. Available from: https://www.techtarget.com/iotagenda/definition/drone.
2. Boon K, Lovelace DC, editors. The domestic use of unmanned aerial vehicles. Terrorism: Commentary on Security Documents. Vol. 134. Oxford University Press; 2014; 207–228.
3. Vitto V. Report of the defense science board task force on the investment strategy for darpa. Washington DC: Defence Science Board; 1999 Jul 1.
4. Matt Parker GB. Quadcopter. Fort Collins, Colorado-80523: Colorado State University; 2012.
5. Weiger RL. Military unmanned aircraft systems in support of homeland security. PA: Army War Coll Carlisle Barracks; 2007 Mar 30.
6. Williams BG. Predators: The CIA’s drone war on Al Qaeda. Potomac Books, Inc.; 2013 Jul 31.
7. Campbell JP. Vertical takeoff and landing aircraft. New York: The MacMillan Company; 1962.
8. Garrett Reim. (2019). DARPA cancels ARES cargo drone project with Lockheed Martin. [Online]. Flight Global. Available from: https://www.flightglobal.com/helicopters/darpa-cancels-ares-cargo- drone-project-with-lockheed-martin-/132645.article.
9. Cohen J. Drone spy plane helps fight California fires. Science. 2007; 318(5851): 727.
10. Roberts MR. 5 drone technologies for firefighting. Fire Chief Magazine; 2014.

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Recent Trends in Programming languages

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[if 344 not_equal=””]ISSN: 2455-1821[/if 344]

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Volume 9
Issue 1
Received May 12, 2022
Accepted May 19, 2022
Published May 25, 2022

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RTPL

Intensifying Security with Smart Video Surveillance

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    Swati Verma, Vikas Jaiswal, Radhey Shyam

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  1. Student, Professor,Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management (SRMCEM), Lucknow, Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management SRMCEM, Lucknow,Uttar Pradesh, Uttar Pradesh,India, India

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n The smart video surveillance system is designed to observe the activities of people nearby for safety concerns. This smart video surveillance can be installed at malls, public areas, banks, businesses, and ATM machines. The field of network surveillance research is rapidly expanding. The cause for this is the global insecurity that is occurring in great extent. As a result, an intelligent monitoring system that gathers data in real time, communicates, analyses, and interprets information about individuals being observed is required. Video surveillance systems are in great demand due to increasing safety concerns. The automated anomaly detection systems are much better since they require almost negligible human intervention and can more accurately detect anomalies. Besides this, the data can be easily stored and retrieved with the help of the video surveillance system. Video surveillance systems may be used for a variety of purposes, including traffic monitoring and anomaly detection. The study describes how three- dimensional deep neural networks are used to learn spatiotemporal elements of video feeds that can be used in our smart surveillance system.

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Keywords: Automated anomaly detection, deep neural networks, spatiotemporal, video surveillance system, CNN

n [if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Programming languages(rtpl)]

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References

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1. Du Z, Wu S, Huang D, Li W, Wang Y. Spatio-temporal encoder-decoder fully convolutional network for video-based dimensional emotion recognition. IEEE Trans Affect Comput. 2019 Sep 10; 12(3): 565–578.
2. Ji S, Xu W, Yang M, Yu K. 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell. 2012 Mar 6; 35(1): 221–31.
3. Sultani W, Chen C, Shah M. Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Conference on computer vision and pattern recognition. 2018; 6479–6488.
4. Sahil Digikar, Abhijit Chaudhari, Pratik Angre, Rajat Pathak. Autoencoder Based Anomaly Detection in Surveillance Videos. Open Access International Journal of Science & Engineering (OAIJSE). 2021; 6: 29–32.
5. Shidik GF, Noersasongko E, Nugraha A, Andono PN, Jumanto J, Kusuma EJ. A systematic review of intelligence video surveillance: Trends, techniques, frameworks, and datasets. IEEE Access. 2019 Nov 25; 7: 170457–73.
6. Chong YS, Tay YH. Abnormal event detection in videos using spatiotemporal autoencoder. In International symposium on neural networks; Springer, Cham. 2017 Jun 21; 189–196.
7. Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS. Learning temporal regularity in video sequences. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016; 733–742.
8. Mahadevan V, Li W, Bhalodia V, Vasconcelos N. Anomaly detection in crowded scenes. In 2010 IEEE computer society conference on computer vision and pattern recognition. 2010 Jun 13; 1975– 1981.
9. Radhey Shyam. Convolutional Neural Network and its Architecture. Journal of Computer Technology and Applications (JoCTA). 2021; 12(2): 6–14.
10. Vartika Srivastava and Radhey Shyam, Enhanced Object Detection with Deep Convolutional Neural Network, International Journal of All Research Education and Scientific Methods, 2021; 9(7): 27-36p.
11. Patraucean V, Handa A, Cipolla R. Spatio-temporal video autoencoder with differentiable memory. arXiv preprint arXiv:1511.06309. 2015 Nov 19.
12. Kiran BR, Thomas DM, Parakkal R. An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J Imaging. 2018 Feb; 4(2): 36.
13. Radhey Shyam. Comparative Analysis of Distance Metrics using Face Recognition. Research & Review: Discrete Mathematical Structures (RRDMS). 2021; 8(1): 1–7.

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Volume 09
Issue 01
Received May 19, 2022
Accepted June 4, 2022
Published June 9, 2022

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