Mobile QR Code QR CODE

※ The user interface design of www.ieiespc.org has been recently revised and updated. Please contact inter@theieie.org for any inquiries regarding paper submission.

Journal Search

  1. Home
  2. Archive
  3. 2024-08 (Vol.13 No.04)
  4. 10.5573/IEIESPC.2024.13.4.414
XML PDF INFO REF

REFERENCES

1 
Jonathan O E, Olusola A J, Bernadin T C A, et al. Impacts of crime on socio-economic development. Mediterranean Journal of Social Sciences, 2021, 12(5): 71.URL
2 
Collins R T, Lipton A J, Kanade T, et al. A system for video surveillance and monitoring. VSAM final report, 2000, (1-68): 1.URL
3 
Socha R, Kogut B. Urban video surveillance as a tool to improve security in public spaces. Sustainability, 2020, 12(15): 6210.DOI
4 
Li X, Lu R, Liang X, et al. Smart community: an internet of things application. IEEE Communications magazine, 2011, 49(11): 68-75.DOI
5 
Barrett B F D, DeWit A, Yarime M. Japanese smart cities and communities: Integrating technological and institutional innovation for Society 5.0. Smart Cities for Technological and Social Innovation. Academic Press, 2021: 73-94.DOI
6 
Yao S, Ardabili B R, Pazho A D, et al. Real-World Community-in-the-Loop Smart Video Surveillance--A Case Study at a Community College. arXiv preprint arXiv:2303.12934, 2023.DOI
7 
Shehzed A, Jalal A, Kim K. Multi-person tracking in smart surveillance system for crowd counting and normal/abnormal events detection. 2019 International conference on applied and engineering mathematics (ICAEM). IEEE, 2019: 163-168.DOI
8 
Bhati, B. S., & Rai, C. S. (2021). Intrusion detection technique using Coarse Gaussian SVM. International Journal of Grid and Utility Computing, 12(1), 27-32.DOI
9 
Bhati, B. S., & Rai, C. S. (2020). Analysis of support vector machine-based intrusion detection techniques. Arabian Journal for Science and Engineering, 45, 2371-2383.DOI
10 
Tiwari, D., & Bhati, B. S. (2021). A deep analysis and prediction of covid-19 in India: using ensemble regression approach. Artificial Intelligence and Machine Learning for COVID-19, 97-109.URL
11 
Weaver III A, Ojiambo W, Kemp J, et al. Pedestrian Walkways: Hidden Hazards Related to Common Landscaping Practices. Professional Safety, 2022, 67(07): 14-22.URL
12 
Li Y, Esmaeili B, Gheisari M, et al. Using Unmanned Aerial Systems (UAS) for Assessing and Monitoring Fall Hazard Prevention Systems in High-rise Building Projects. arXiv preprint arXiv:2209, 13137, 2022.DOI
13 
Ang G C, Low S L, How C H. Approach to falls among the elderly in the community. Singapore medical journal, 2020, 61(3): 116.DOI
14 
Vaishya R, Vaish A. Falls in older adults are serious. Indian journal of orthopedics, 2020, 54: 69-74.DOI
15 
Johnson J, Rodriguez M A, Al Snih S. Life-space mobility in the elderly: current perspectives. Clinical interventions in aging, 2020: 1665-1674.DOI
16 
Carpenter C R, Cameron A, Ganz D A, et al. Older adult falls in emergency medicine: 2019 update. Clinics in geriatric medicine, 2019, 35(2): 205-219.DOI
17 
Tanwar R, Nandal N, Zamani M, et al. Pathway of trends and technologies in fall detection: a systematic review. Healthcare. MDPI, 2022, 10(1): 172.DOI
18 
Ramanujam E, Padmavathi S. A vision-based posture monitoring system for the elderly using intelligent fall detection technique. Guide to Ambient Intelligence in the IoT Environment: Principles, Technologies and Applications, 2019: 249-269.DOI
19 
Mirmahboub B, Samavi S, et al. Automatic monocular system for human fall detection based on variations in silhouette area. IEEE transactions on bio medical engineering, 2013, 60(2):427-436.DOI
20 
Ma X, Wang H, et al. Depth-Based human fall detection via shape features and improved extreme Learning Machine. IEEE Journal of Biomedical and Health Informatics,2014,18(6):1915-1922.DOI
21 
Harrou F, Zerrouki N, Sun Y, et al. Vision-based fall detection system for improving safety of elderly people. IEEE Instrumentation and Measurement Magazine, 2017, 20(6):49-55.DOI
22 
Chen W, Jiang Z, Guo H, et al. Fall detection based on key points of human-skeleton using Open Pose. Symmetry, 2020, 12(5): 744.DOI
23 
Osokin D. Real-time 2d multi-person pose estimation on cpu: Lightweight openpose. arXiv preprint arXiv:1811.12004, 2018.DOI
24 
Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3. Proceedings of the IEEE/CVF international conference on computer vision. 2019: 1314-1324.URL
25 
Scholkopf B, Mika S, Burges C J C, et al. Input space versus feature space in kernel-based methods. IEEE transactions on neural networks, 1999, 10(5): 1000-1017.DOI
26 
Weng M, Huang G, Da X. A new interframe difference algorithm for moving target detection. 2010 3rd international congress on image and signal processing. IEEE, 2010, 1: 285-289.DOI
27 
Weinstein R. RFID: a technical overview and its application to the enterprise. IT professional, 2005, 7(3): 27-33.DOI
28 
Ringberg H, Soule A, Rexford J, et al. Sensitivity of PCA for traffic anomaly detection. Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems. 2007: 109-120.DOI
29 
Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort-Asturiano, C. (2019). UP-fall detection dataset: A multimodal approach. Sensors, 19(9), 1988.DOI