SMART CROWD MONITORING SYSTEM USING IOT-BASED YOLO-GHOST
DOI:
https://doi.org/10.59277/RRST-EE.2024.69.3.15Keywords:
Internet of things (IoT), Crowd monitoring system, Deep learning, YOLO-GHOSTAbstract
Internet of Things (IoT) devices offer a smart, sophisticated, real-time surveillance solution for public spaces. However, due to instantaneous lighting changes and varying viewing angles, counting and tracking the people in crowded scenes is a challenging problem. To combat these issues, a novel YOLO-CROWD is proposed for a smart crowd-monitoring system using YOLO-GHOST. Initially, an Internet Protocol (IP) based camera was used to monitor and capture crowds in a video sequence. The captured video sequences are converted into frames and passed to the server via the Internet. The recorded frames are given to the YOLO-GHOST classification module to perform people detection and counting. Finally, the detected output results are transferred to the surveillance monitoring center's server. The YOLO-CROWD technique is simulated by using MATLAB. The effectiveness of the proposed YOLO-CROWD technique is assessed using evaluation metrics such as accuracy, precision, recall, sensitivity, F1-score, and mean average precision. The experimental results show that the accuracy of the YOLO-CROWD has increased to up to 99.95 %, proving that its intended use is for accurate crowd detection. The detection accuracy of the proposed method is 84.9 %, 87.58 %, 93.91 %, and 97.72 % better than existing EABeD, LCDnet, CDEM-M, and Public Vision, respectively.
References
(1) N. Thakur, P. Nagrath, R. Jain, D. Saini, N. Sharma, D.J. Hemanth, Autonomous pedestrian detection for crowd surveillance using deep learning framework, Soft Computing, 27, 14, pp. 9383-9399 (2023).
(2) M.Ş. Gündüz, G. Işık, A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models. Journal of Real-Time Image Processing, 20, 1 (2023).
(3) A.K. Suhane, A.V.H.P.U. Raghuwanshi, A. Nimbark, L. Saxena, Human detection and crowd counting using Yolo, Conference: Tech Fest IIT Roorkee (2023).
(4) S. Zheng, J. Wu, S. Duan, F. Liu, J. Pan, An improved crowd counting method based on YOLOv3, Mobile Networks, and Applications, pp. 1–9 (2022).
(5) S. Mohanapriya, P. Natesan, K. Rinisha, S. Nishanth, J. Robin, Video segmentation using YOLOv5 for surveillance, 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), pp. 1–5 (2023).
(6) F. Liu, Z. Han, H. Song, J. Wang, C. Liu, G. Ban, Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos, Transactions in GIS, 27, 2, pp. 494-515 (2023).
(7) A. Alamri, Cloud of things in crowd engineering: a tile-map-based method for intelligent monitoring of outdoor crowd density, Sensors, 22, 9, pp. 3328 (2022).
(8) S. Wang, Z. Pu, Q. Li, Y. Wang, Estimating crowd density with edge intelligence based on lightweight convolutional neural networks, Expert Systems with Applications, 206, pp. 117823 (2022).
(9) T. Motedayen, M. Yaghoobi, M. Kheirabadi, Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm, Journal of Control and Decision, pp. 1–14 (2022).
(10) M.P. Kantipudi, R. Aluvalu, S. Velamuri, An intelligent approach of intrusion detection in mobile crowd sourcing systems in the context of IoT based SMART city, Smart Science, 11, 1, pp. 234–240 (2023).
(11) F. Aldosari, S. Alamri, R. Alharbi, G. Alghamdi, IoT based crowd management tool, Proceedings of SAI Intelligent Systems Conference, pp. 533–539 (2022).
(12) A.I. Middya, P. Dey, S. Roy, IoT-based crowdsensing for smart environments, Internet of Things for Smart Environments, pp. 33–58), Springer International Publishing (2022).
(13) H. Zhao, W. Min, J. Xu, Q. Wang, Y. Zou, Q. Fu, Scene-adaptive crowd counting method based on meta learning with dual-input network DMNet. Frontiers of Computer Science, 17, 1, 171304 (2023).
(14) L. Zhang, L. Yan, M. Zhang, J. Lu, T2CNN: a novel method for crowd counting via two-task convolutional neural network, The Visual Computer, 39, 1, pp. 73–85 (2023).
(15) S.S. Savner, V. Kanhangad, CrowdFormer: weakly-supervised crowd counting with improved generalizability, Journal of Visual Communication and Image Representation, 94, 103853 (2023).
(16) I. Ahmed, M. Ahmad, A. Ahmad, G. Jeon, IoT-based crowd monitoring system: using SSD with transfer learning, Computers & Electrical Engineering, 93,107226 (2021).
(17) W. Xiao, M. Li, B. Alzahrani, R. Alotaibi, A. Barnawi, Q. Ai, A blockchain-based secure crowd monitoring system using UAV swarm. IEEE Network, 35, 1, pp. 108–115 (2021).
(18) L. Rajendran, R.S. Shankaran, Bigdata enabled realtime crowd surveillance using artificial intelligence and deep learning, IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 129–132 (2021).
(19) M.U. Farooq, M.N.M. Saad, S.D. Khan, Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd, The Visual Computer, 38, 5, pp.1553–1577 (2022).
(20) H. Choi, M. Fujimoto, T. Matsui, S. Misaki, K. Yasumoto, Wi-cal: Wifi sensing and machine learning based device-free crowd counting and localization, IEEE Access, 10, pp. 24395–24410 (2022).
(21) S. Wang, Z. Pu, Q. Li, Y. Wang, Estimating crowd density with edge intelligence based on lightweight convolutional neural networks, Expert Systems with Applications, 206, 117823 (2022).
(22) Y. Zhen, M. Sugasaki, Y. Kawahara, K. Tsubouchi, M. Ishige, H. Murakami, M. Shimosaka, Efficient adaptive beacon deployment optimization for indoor crowd monitoring applications, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6, 4, pp. 1–22 (2023).
(23) M.A. Khan, H. Menouar, and R. Hamila, LCDnet: a lightweight crowd density estimation model for real-time video surveillance, Journal of Real-Time Image Processing, 20, 2, 29 (2023).
(24) X. Zhang, Y. Sun, Q. Li, X. Li and X. Shi, Crowd density estimation and mapping method based on surveillance video and GIS, ISPRS International Journal of Geo-Information, 12, 2, 56 (2023).
(25) M. Qaraqe, A. Elzein, E. Basaran, Y. Yang, E.B. Varghese, W. Costandi, J. Rizk, N. Alam, PublicVision: a secure smart surveillance system for crowd behavior recognition, IEEE Access, 12, pp. 26474-26491 (2024).
(26) G.C. Serițan, B.A. Enache, I. Vilciu, S.D. Grigorescu, V. Mladenov, Comparison study of top development boards in the context of Iot, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 67, 4, pp. 483–486 (2022).
(27) R.R. Sathiya, S. Rajakumar, J. Sathiamoorthy, Secure blockchain-based deep learning approach for data transmission in IOT-enabled healthcare system, International Journal of Computer and Engineering Optimization, 01, 01, pp. 15–23 (2023).
(28) S. Ramamoorthi, A. Appathurai, Energy-aware clustered blockchain data for IoT: an end-to-end lightweight secure & Enroute filtering approach, Computer Communications, 202, pp. 166 –182 (2023).
(29) S. Reeba Rex, T. Pravin Rose, S. Amudaria, Real-time remote monitoring via horse head optimization deep learning network, International Journal of Data Science and Artificial Intelligence, 02, 02, pp. 42–47 (2024).
(30) R. Sundarasekar, A. Appathurai, Automatic brain tumor detection and classification based on IoT and machine learning techniques, Fluctuation and Noise Letters, 21, 03, 2250030 (2022).
(31) R.A. Crăciun, R.N. Pietraru, M.A. Moisescu, Internet of Things Platform Benchmark: An Artificial Intelligence Assessment, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 69, 1, pp. 97–102 (2024).
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