Abstract
Today, we live in a less secure world. That being said we are constantly under some threat, be it accidents on road or robbery, etc. There are number of security systems which are installed to tackle these problems. Instead, they record video and consume memory. It does not give any implication about the incident. To tackle these problems a real-time suspicious activity detection system should be developed. This system will have an advantage over conventional system as it will continuously monitor the frame from particular camera. This can be implemented in any field using less amount of hardware. The system which we are designing is used to monitor the events taking place in frame of camera using image processing. In this proposed method, we are using a Raspberry Pi as our main processor to which camera will be interfaced.
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References
Bermejo, E., Déniz, O., Bueno, G.: Security system based on suspicious behavior detection. In: Buran. Universitat Politecnica De Catalunya Barcelonatech, vol. 25, pp. 12–16. Catalonia, Spain (2010)
Chaitanya, K., Karudaiyar, G., Deepak, C., Reddy, S.B.: Multiple home automation on Raspberry Pi. In: Satapathy, S., Bhateja, V., Joshi, A. (eds.) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol. 469. Springer, Singapore (2017)
Rajenderana, S.V., Ka Fei, T.: Real-time detection of suspicious human movement. In: International Conference on Electrical, Electronics, Computer Engineering and their Applications. Lumpur, Malaysia (2014). https://doi.org/10.1109/ICASSP.2013.6638290.Kuala
Guo, T., Dong, J.: Simple convolutional neural network on image classification. In: IEEE 2nd International Conference on Big Data Analysis (ICBDA). Beijing, China (2017). https://doi.org/10.1109/ICBDA.2017.8078730
Hou, X., Shang, Y., Liu, H., Song, Q.: Research on the real-time image edge detection algorithm based on FPGA. In: Shen, G., Huang, X. (eds.) Advanced Research on Computer Science and Information Engineering, CSIE 2011. Communications in Computer and Information Science, vol. 153. Springer, Heidelberg (2011)
Torres, P., Malhao, S.: Error correction repetition codes with Arduino and Raspberry Pi. In: Machado, J., Soares, F., Veiga, G. (eds.) Innovation, Engineering and Entrepreneurship. HELIX 2018. Lecture Notes in Electrical Engineering, vol. 505. Springer, Cham (2019)
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Sarang, S., Shinde, H., Raut, V., Sonje, S., Phadke, G. (2020). Real-Time Suspicious Activity Detection. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_43
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DOI: https://doi.org/10.1007/978-981-15-2475-2_43
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Online ISBN: 978-981-15-2475-2
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