Abstract
According to FBI Uniform Crime Reporting (UCR) statistics per year, there are roughly 2.5 million property-related burglaries, and out of which, the police solved only 13% of these reports due to the lack of a witness. Traditional methods such as using a CCTV surveillance system require a person to constantly monitor those cameras. This may be an effective way in locations such as offices and other public places. However, it cannot be used in houses as it would not only breach the privacy of the home owners but also not an effective way to detect an intrusion. In this paper, we propose eliminating the human aspect of monitoring cameras by implementing an autonomous intruder detection and tracking system. This system contains an indoor unit and an outdoor unit, and these two units communicate with each other using TCP/IP sockets. The indoor units contain a single camera present inside the house which uses face recognition to detect intruder. When the intruder leaves the house, the outdoor unit which contains multiple cameras located outside the house uses the image of the intruder taken by the indoor unit to look for the intruder. These two units use SMTP protocol to send email alerts when the intruder is detected. The indoor unit autonomously detects the intruder, while the outdoor unit autonomously locates the intruder. The salient feature is that by implementing this system, we can offset the lack of presence of a physical witness and aid the police in catching the intruder.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Khan SA, Ahmad I, Shifian A, Ahmed MS (2019) A smart intruder alert system based on microprocessor and motion detection. In: 2019 international conference on robotics, electrical, and signal processing techniques (ICREST), 2019, pp 335–339
Shahid A et al (2017) Computer vision based intruder detection framework. In: 2017 2nd international conference on computer and communication systems, 2017, pp 41–45
Shaikh MU, Vora D, Anurag A (2021) Surveillance system for intruder detection using facial recognition. In: Balas VE, Semwal VB, Khandare A, Patil M (eds) Intelligent computing and networking. Lecture notes in networks and systems, vol 146. Springer, Singapore. https://doi.org/10.1007/978-981-15-7421-4_18
Uday BK, Vattikuti A, Gogineni K, Natarajan P (2019) Machine learning techniques for automatic intruder identification and alerting. In Saini H, Sayal R, Govardhan A, Buyya R (eds) Innovations in computer science and engineering. Lecture notes in networks and systems, vol 74, Springer, Singapore
Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of convolutional neural network. In: 2017 international conference on engineering and technology (ICET), 2017, pp 1–6
Sharma S, Shanmugasundaram K, Ramasamy SK (2016) FAREC-CNN based efficient face recognition technique using Dlib. In: International conference on advanced communication control and computing technologies (ICACCCT), 2016, pp 192–195. https://doi.org/10.1109/ICACCCT.2016.7831628
https://devdoc.net/c/dlib-19.7/python/ddlib.cnn_face_detection_model_v1
Boyko N, Basystiuk O, Shakhovska N (2018) Performance evaluation and comparison of software for face recognition, based on Dlib and Opencv library. In: IEEE second international conference on data stream mining & processing (DSMP), 2018, pp 478–482. https://doi.org/10.1109/DSMP.2018.8478556
Cahyono F, Wirawan W, Fuad Rachmadi R (2020) Face recognition system using Facenet algorithm for employee presence. In: 2020 4th international conference on vocational education and training (ICOVET), 2020, pp 57–62. https://doi.org/10.1109/ICOVET50258.2020.9229888
Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 539–546. https://doi.org/10.1109/CVPR.2005.202
Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant map**. In: 2006 IEEE Computer Society conference on computer vision and pattern recognition (CVPR’06), 2006, pp 1735–1742. https://doi.org/10.1109/CVPR.2006.100
https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
Sureswaran R, Bazar HA, Abouabdalla O, Manasrah AM (2009) Active E-mail system protocols monitoring algorithm. In: TENCON 2009—2009 IEEE region 10 conference, 2009, pp 1–6. https://doi.org/10.1109/TENCON.2009.5396120
Hunt J (2019) Sockets in Python. In: Advanced guide to Python 3 programming. Undergraduate topics in computer science. Springer, Cham. https://doi.org/10.1007/978-3-030-25943-3_39
Arora P, Dumka A (2018) Review of sockets for transfer of files between systems. In: Singh R, Choudhury S, Gehlot A (eds) Intelligent communication, control and devices. Advances in intelligent systems and computing, vol 624. Springer, Singapore. https://doi.org/10.1007/978-981-10-5903-2_152
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Abhinay, D., Chaitanya, K., Ram, P.S. (2022). Intruder Detection and Tracking Using Computer Vision and IoT. In: Kumar Jain, P., Nath Singh, Y., Gollapalli, R.P., Singh, S.P. (eds) Advances in Signal Processing and Communication Engineering. Lecture Notes in Electrical Engineering, vol 929. Springer, Singapore. https://doi.org/10.1007/978-981-19-5550-1_44
Download citation
DOI: https://doi.org/10.1007/978-981-19-5550-1_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5549-5
Online ISBN: 978-981-19-5550-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)