IOT Based Video Surveillance System
DOI:
https://doi.org/10.55524/Keywords:
Surveillance, AIOT, Motion Detection, YOLO, AuAbstract
In present scenario, the security concerns have grown tremendously. The security of restricted areas such as borders, is of utmost importance, in particular with the worldwide increase of military conflicts, illegal immigrants, and terrorism over the past decade. Monitoring such areas rely currently on technology and manpower, however automatic monitoring has been advancing in order to avoid potential human errors that can be caused by different reasons. The purpose of this work is to design a surveillance system which would detect motion in a live video feed and record the video feed only now where the motion was detected, also to track moving objects based on background subtraction using video surveillance. The moving object is identified using the image subtraction method through machine learning based Algorithms (i.e., like keras and OpenCV). This work is based on AIOT (Artificial Intelligence of Things which is a combination of Artificial Intelligence and Internet of Things).
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B. S. o. N. I. Daniela D’Auria, "SARRI: a SmArt Rapiro Robot Integrating a framework for automatic high-level surveillance," IEEE International Conference on Robotic Computing, 2018.
a. J. C. M. Yoon, "Design and Implementation of an Advanced Cattle Shed Management System using a Infrared Wireless Sensor nodes and Surveillance Camera," JKCA, vol. 12, no. 10, 2012.
K. C. a. D.-H. L. Jongnam Bae, "Speed and Direction Control of Two In-wheel BLDC Motors for the Self-Driving Surveillance Robot," in International Conference on Mechatronics and Robotics Engineering, Busan, 2020.
J. H. a. J. Z. J. Lu, "Deep metric learning for visual understanding An overview of recent advances," SPM, vol. 34, no. 6, pp. 76-84, 2017.
J. L. S. M. I. J. F. M. L. Ren, "Uniform and Variational Deep Learning for RGB-D Object Recognition and Person Re Identification," IEEE, no. 10.1109/TIP.2019.2915655,, no. 2019, p. 10.
J. W. F. a. T. D. G. Shakhnarovich, "Face recognition from long-term observations," Proceedings in Europe. Confrence of Computer Vision Lecture, vol. 2352, pp. 851-865, 2002.
J. K. a. R. C. T.-K. Kim, "Discriminative learning and recognition of image set classes using canonical correlations," IEEE Trans. Pattern Anal. Mach. Intell , vol. 29, no. 6.
J. K. a. R. C. T.-K. Kim, "Discriminative learning and recognition of image set classes using canonical correlations," in IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6
K. He, "Spatial pyramid pooling in deep convolutional networks for visual recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, pp. 1904- 1916, 2014.
J. Redmon and A. Farhadi, "Yolo9000: better, faster, stronger," 2016.
Y. C. a. L. C. H. Wang, "A vehicle detection algorithm based on deep belief network," Ae Scientific World Journal, no. 2014, 2014.
S. L. J.-Y. K. M. K. a. H.-J. Y. K. Kim, "A configurable heterogeneous multicore architecture with cellular neural network for realtimeobject recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 11, pp. 1612-1622, 2009.
A. R. M. a. P. K. M. N. Sudha, "A self-configurable systolic architecture for face recognition system based on principal component neural network," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 8, pp. 1071- 1084, 2011.
J. T. S. J. W. a. M. R. M. Blum, "A learned feature descriptor for object recognition in rgb-d data," ICRA, pp. 1298-1303, 2012.
G. S. J. F. R. C. a. T. D. O. A. ́, "Face recognition with image sets using manifold density divergence," IEEE Institute Confrence of Computer Vision and Pattern Recognition,, 2005
G. S. J. F. R. C. a. T. D. O. A. ́and, "Face recognition with image sets using manifold density divergence," in IEEE Institute Confrence of Computer Vision and Pattern Recognition, 2005.
W. F. a. D.-Y. Yeung, "Face recognition with image sets using hierarchically extracted exemplars from appearance manifolds," in Proc. Int. Conf. Automatic Face and Gesture Recognition, 2006.
J. H. a. J. Z. J. Lu, "Deep metric learning for visual understanding An overview of recent advances," SPM, vol. 34, no. 6, pp. 76-84, 2017.
Kumar, Ch Manohar, and N. Kumar Muvvala. "A compact ultra-wide band rhombus shaped fractal antenna with metamaterial in the ground plane." Int. J. Eng. Adv. Technol.(IJEAT) 8, no. 6 (2019)..
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