The week's pick
Reseach Article
Video Analyzer for Social Distancing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 1 |
| Year of Publication: 2021 |
| Authors: Shivani Salokhe, Yadnyi Deshpande, Shivani Datar |
10.5120/ijca2021921284
|
Shivani Salokhe, Yadnyi Deshpande, Shivani Datar . Video Analyzer for Social Distancing. International Journal of Computer Applications. 183, 1 ( May 2021), 51-55. DOI=10.5120/ijca2021921284
Abstract
The use of social distance as a barrier to the spread of the infectious Coronavirus Disease has been shown to be successful (COVID-19). Individuals, on the other hand, are not accustomed to keeping track of the necessary 6-foot distance between themselves and their surroundings. The spread of this deadly disease could be delayed by an active surveillance system capable of detecting distances between individuals. The paper proposes an AI-based system for automating the task of monitoring social distancing through surveillance. Creating a video analyzer platform to help authorities ensure that social distancing standards are observed in public areas. The tool will monitor if people wear masks and social distancing is maintained in public areas.
References
- Yang, Dongfang, et al. ‘A Vision-Based Social Distancing and Critical Density De- tection System for COVID-19’. ArXiv:2007.03578 [Cs, Eess], July 2020. arXiv.org, http://arxiv.org/abs/2007.03578.. .
- Rezaei, Mahdi, and Mohsen Azarmi. ‘DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic’. ArXiv:2008.11672 [Physics], Aug. 2020. arXiv.org, http://arxiv.org/abs/2008.11672.
- Ghorai, Arnab, et al. Digital Solution for Enforcing Social Distancing. SSRN Scholarly Paper, ID 3614898, Social Science Research Network, 31 May 2020. papers.ssrn.com,https://papers.ssrn.com/abstract=3614898
- Cristani, Marco, et al. ‘The Visual Social Distancing Problem’. ArXiv:2005.04813 [Cs, Eess], May 2020. arXiv.org, http://arxiv.org/abs/2005.04813..
- Du, Juan. ‘Understanding of Object Detection Based on CNN Family and YOLO’. Journal of Physics: Conference Series, vol. 1004, Apr. 2018, p. 012029. DOI.org (Crossref), doi:10.1088/1742-6596/1004/1/012029..
- Girshick, Ross. ‘Fast R-CNN’. ArXiv:1504.08083 [Cs],Sept. 2015. arXiv.org, http://arxiv.org/abs/1504.08083.
- Redmon, Joseph, and Ali Farhadi. ‘YOLOv3: An Incremental Improvement’. ArXiv:1804.02767 [Cs], Apr. 2018. arXiv.org, http://arxiv.org/abs/1804.02767.
- Redmon, Joseph, et al. ‘You Only Look Once: Unified, Real-Time Object Detection’. ArXiv:1506.02640 [Cs], May 2016. arXiv.org, http://arxiv.org/abs/1506.02640.
- Degtyarev, N., and O. Seredin. ‘Comparative Testing of Face Detection Algorithms’. ICISP, 2010. Semantic Scholar, doi:10.1007/978-3-642-13681-8_24.
- Li, Jian, et al. ‘DSFD: Dual Shot Face Detector’. ArXiv:1810.10220 [Cs], Apr. 2019. arXiv.org, http://arxiv.org/abs/1810.10220.
- Ku, Hongchang, and Wei Dong. Face Recognition Based on MTCNN and Convolutional Neural Network. 2020. Semantic Scholar, doi:10.22606/fsp.2020.41006.
- Schubert, Erich, et al. ‘DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN’. ACM Transactions on Database Systems, vol. 42, no. 3, July 2017, p. 19:1–19:21. August 2017, doi:10.1145/3068335.
- Wang, Xiang, et al. ‘A Survey on Face Data Augmentation’. Neural Computing and Applications, vol. 32, no. 19, Oct. 2020, pp. 15503–31. arXiv.org, doi:10.1007/s00521-020-04748-3.
- Kollias, Dimitrios, et al. ‘Deep Neural Network Augmentation: Generating Faces for Affect Analysis’. International Journal of Computer Vision, vol. 128, no. 5, May 2020, pp. 1455–84. DOI.org (Crossref), doi:10.1007/s11263-020-01304-3
Index Terms
Keywords