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Surveillance Violence Detection System
Surveillance security may be a terribly tedious and long job. In this project, we build a system to automatize the task of analyzing video. we will analyze the video which we put in our model and the model determine any abnormal activities like violence. There are tons of analysis happening within the trade concerning video. Recent increased adaptation of security cameras. This paper describes a recognition and identify system for abnormal objects. The goal is to design and implement a system which will be able to detect abnormal activity using video sequences. The system uses high level reasoning to infer the existence of abnormal activity. The proposed approach was implemented using existing images, video clips and trained video. Our experiments demonstrate the effectiveness of the approach.
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- Dhiman and D. K. Vishwakarma, “A review of state-of the-art techniques for abnormal human activity recognition,” Engineering Applications of Artificial Intelligence, 2019.
- W. Sultani, C. Chen, and M. Shah, “Real-world Anomaly Detection in Surveillance Videos,” Centre for Research in Computer Vision (CRCV), University of Central Florida (UCF), 2018.
- S. Chaudhary, M. A. Khan, and C. Bhatnagar, “Multiple Anomalous Activity Detection in Videos,” Procedia Computer Science, 2018.
- L. Tian, H. Wang, Y. Zhou, and C. Peng, “Video big data in smart city: Background construction and optimization for surveillance video processing,” Future Generation Computer Systems, 2018.
- S. Chaudhary, M. A. Khan, and C. Bhatnagar, “Multiple Anomalous Activity Detection in Videos,” Procedia Computer Science, vol. 125, pp. 336–345, 2018.
- A. Ben Mabrouk and E. Zagrouba, “Abnormal behavior recognition for intelligent video surveillance systems: A review,” Expert Systems with Applications, vol. 91, pp. 480–491, 2018.
- Z. Mushtaq, G. Rasool, and B. Shehzad, “Multilingual Source Code Analysis: A Systematic Literature Review,” IEEE Access, vol. 5, pp. 11307–11336, 2017.
- R. Olmos, S. Tabik, and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” rocomputing, vol. 275, pp. 66–72, 2018.
- P. Zhou, Q. Ding, H. Luo, and X. Hou, “Violent Interaction Detection in Video Based on Deep Learning” in Journal of Physics: Conference Series, 2017.
- P. C. Ribeiro, R. Audigier, and Q. C. Pham, “RIMOC, a feature to discriminate unstructured motions: application to violence detection for video-surveillance,” Computer vision and image understanding, vol. 144, pp. 121–143, 2016.
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