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Weapon Detection in Surveillance Video
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
The automatic detection of weapons captured by surveillance settings is essential for speeding the otherwise laborious approach. With use of, UCF Crime which is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-Crime, a subset of the UCF-Crime dataset suitable and use a technique for specified object tracking.
References
[1] Dongkuan Xu and Yingjie Tian. A comprehensive survey of clustering algorithms. Annals of Data Science, 2(2):165–193, 2015.
[2] Christophe Guyeux, Ste´phane Chre´tien, Gaby Bou Tayeh, Jacques Demerjian, and Jacques Bahi. Introducing and comparing recent clustering methods for massive data management in the internet of things. Journal of Sensor and Actuator Networks, 8(4):56 (25), dec 2019.
[3] Kristina P Sinaga and Miin-Shen Yang. Unsupervised k-means clustering algorithm. IEEE Access, 8:80716–80727, 2020.
[4] Adil Fahad, Najlaa Alshatri, Zahir Tari, Abdullah Alamri, Ibrahim Khalil, Albert Y Zomaya, Sebti Foufou, and Abdelaziz Bouras. A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE transactions on emerging topics in computing, 2(3):267–279, 2014.
[5] Maria Camila N Barioni, Humberto Razente, Alessandra MR Marcelino, Agma JM Traina, and Caetano Traina Jr. Open issues for partitioning clustering methods: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(3):161–177, 2014.
[6] Mohammad Alhawarat and M Hegazi. Revisiting k- means and topic modeling, a comparison study to cluster arabic documents. IEEE Access, 6:42740–42749, 2018.
[7] Yinfeng Meng, Jiye Liang, Fuyuan Cao, and Yijun He. A new distance with derivative information for func- tional k-means clustering algorithm. Information Sciences, 463:166–185, 2018.
[8] A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society : Series B, 39(1):1–38, 1977.
How to cite this paper
@article{1703965,
author = {Prashant Bhalla, Sachin Garg},
title = {Weapon Detection in Surveillance Video},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {6},
pages = {295-298},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703965.pdf},
abstract = {The automatic detection of weapons captured by surveillance settings is essential for speeding the otherwise laborious approach. With use of, UCF Crime which is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-Crime, a subset of the UCF-Crime dataset suitable and use a technique for specified object tracking.},
month = {December},
}