Abstract
The present document represents a thorough study of the making of an efficient surveillance system along with a feature of automatically informing the owner about the suspicious movement. In this moving world, normally people are suffering from the availability of time, so if any crime has happened at the site, it will take many days of searching for finding the actual presence of criminals, and thus a good chance for those burglars to flee away to protect themselves. For making the task possible, chose Python as the weapon for this battle and used different efficient techniques like COCO dataset for getting labeled and annotated images, LabelImg for making the annotation set of images, TensorFlow, object detection API for object detection and faster RCNN for training as faster RCNN has shown the highest accuracy for the COCO dataset so far. The owner can be informed in two ways: Either send a message to him via mail or phone or call at the time of suspicious image capturing. Here, both of these cases are used: For mail, the task is done via SMTP and for phone calls Twilio is used which provides us registered phone no. and can make both outbound and inbound calls. After using all the mentioned things and making the model in a way described above, it was found that faster RCNN is much more accurate than the other conventional methods. The results have been very well as RCNN show 86.7% accuracy and 100% has come out with the informing module as there simply the mail will be sent to the one whose mail is given in the code and the same is for Twilio calling.
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
References
Kumar H, Bhattacharya S, Thomas SS, Gupta S, Venkatesh KS (2017) Design of smart video surveillance system for indoor and outdoor scenes. In: 2017 22nd international conference on digital signal processing (DSP), London, pp 1–5. https://doi.org/10.1109/ICDSP.2017.8096120
Xu Z, Wu HR (2010) Smart video surveillance system. In: 2010 IEEE international conference on industrial technology, Vina del Mar, pp 285–290. https://doi.org/10.1109/ICIT.2010.5472694
Rai M, Husain AA, Maity T, Yadav RK (2018) Advance intelligent video surveillance system (AIVSS): a future aspect. In: Intelligent video surveillance. IntechOpen. https://doi.org/10.5772/intechopen.76444
Galvez RL, Bandala AA, Dadios EP, Vicerra RRP, Maningo JMZ (2018) Object detection using convolutional neural networks. In: TENCON 2018—2018 IEEE region 10 conference, Jeju, Korea (South), pp 2023–2027. https://doi.org/10.1109/TENCON.2018.8650517
Sudha N (2015) Enabling seamless video processing in smart surveillance cameras with multicore. In: 2015 international conference on advanced computing and communications (ADCOM), Chennai, pp 27–32. https://doi.org/10.1109/ADCOM.2015.12
Raghunandan A, Mohana, Raghav P, Aradhya HVR (2018) Object detection algorithms for video surveillance applications. In: 2018 international conference on communication and signal processing (ICCSP), Chennai, pp 0563–0568. https://doi.org/10.1109/ICCSP.2018.8524461
Zhou X, Gong W, Fu W, Du F (2017) Application of deep learning in object detection. In: 2017 IEEE/ACIS 16th international conference on computer and information science (ICIS), Wuhan, pp 631–634. https://doi.org/10.1109/ICIS.2017.7960069
Zhang J, Gao J, Liu W (2001) Image sequence segmentation using 3-D structure tensor and curve evolution. IEEE Trans Circ Syst Video Technol 11(5):629–641. https://doi.org/10.1109/76.920192
Krotosky SJ, Trivedi MM (2008) person surveillance using visual and infrared imagery. IEEE Trans Circ Syst Video Technol 18(8):1096–1105. https://doi.org/10.1109/TCSVT.2008.928217
Zhiqiang W, Jun L (2017) A review of object detection based on convolutional neural network. In: 2017 36th Chinese control conference (CCC), Dalian, pp 11104–11109. https://doi.org/10.23919/ChiCC.2017.8029130
ShaiCheng P, HuiMin Y (2015) Design of video surveillance platform based on soft switch technology. In: 2015 IEEE international conference on grey systems and intelligent services (GSIS), Leicester, pp 490–493. https://doi.org/10.1109/GSIS.2015.7301906
Xenya MC, Kwayie C, Quist-Aphesti K (2019) Intruder detection with alert using cloud based convolutional neural network and Raspberry Pi. In: 2019 international conference on computing, computational modelling and applications (ICCMA), Cape Coast, Ghana, pp 46–464. https://doi.org/10.1109/ICCMA.2019.00015
Jmour N, Zayen S, Abdelkrim A (2018) Convolutional neural networks for image classification. In: 2018 international conference on advanced systems and electric technologies (ICASET), Hammamet, pp 397–402. https://doi.org/10.1109/ASET.2018.8379889
Li Y, Guo P, Xin X (2016) A divide and conquer method for automatic image annotation. In: 2016 12th international conference on computational intelligence and security (CIS), Wuxi, pp 660–664. https://doi.org/10.1109/CIS.2016.0159
Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET), Antalya, pp 1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186
Chauhan R, Ghanshala KK, Joshi RC (2018) Convolutional neural network (CNN) for image detection and recognition. In: 2018 first international conference on secure cyber computing and communication (ICSCCC), Jalandhar, India, pp 278–282. https://doi.org/10.1109/IC-SCCC.2018.8703316
Yang J, Li J (2017) Application of deep convolution neural network. In: 2017 14th international computer conference on wavelet active media technology and information processing (ICCWAMTIP), Chengdu, pp 229–232. https://doi.org/10.1109/ICCWAMTIP.2017.8301485
Sureswaran R, Bazar HA, Abouabdalla O, Manasrah AM, El-Taj H (2009) Active e-mail system SMTP protocol monitoring algorithm. In: 2009 2nd IEEE international conference on broadband network and multimedia technology, Beijing, pp 257–260. https://doi.org/10.1109/ICB-NMT.2009.5348490
Venkatesan S, Jawahar A, Varsha S, Roshne N (2017) Design and implementation of an automated security system using Twilio messaging service. In: 2017 international conference on smart cities, automation &intelligent computing systems (ICON-SONICS), Yogyakarta, pp 59–63. https://doi.org/10.1109/ICON-SONICS.2017.8267822
Li X et al (2019) COCO-CN for cross-lingual image tagging, captioning, and retrieval. IEEE Trans Multimedia 21(9):2347–2360. https://doi.org/10.1109/TMM.2019.2896494
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tyagi, E. et al. (2021). Video Surveillance System with Auto Informing Feature. In: Sharma, H., Saraswat, M., Kumar, S., Bansal, J.C. (eds) Intelligent Learning for Computer Vision. CIS 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 61. Springer, Singapore. https://doi.org/10.1007/978-981-33-4582-9_32
Download citation
DOI: https://doi.org/10.1007/978-981-33-4582-9_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4581-2
Online ISBN: 978-981-33-4582-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)