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A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

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Abstract

The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues.

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References

  1. Al-Zaydi, Z., Vuksanovic, B., Habeeb, I.: Image processing based ambient context-aware people detection and counting. Int. J. Mach. Learn. Comput. 8(3), 268–273 (2018)

    Article  Google Scholar 

  2. Almalki, K.J., Choi, B.Y., Chen, Y., Song, S.: Characterizing scattered occlusions for effective dense-mode crowd counting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3840–3849 (2021)

  3. Amirgholipour, S., Jia, W., Liu, L., Fan, X., Wang, D., He, X.: Pdanet: pyramid density-aware attention based network for accurate crowd counting. Neurocomputing 451, 215–230 (2021)

    Article  Google Scholar 

  4. Babu, M.G., Bhalaji, C., Rajendran, S., Selvi, V.A.I.: IoT based crowd estimation and stranger recognition in closed public areas. In: 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 763–773. IEEE, Piscataway (2021)

  5. Bai, H., Mao, J., Chan, S.H.G.: A survey on deep learning-based single image crowd counting: network design, loss function and supervisory signal. Neurocomputing 508, 1–18 (2022)

    Article  Google Scholar 

  6. Barbosa, L.N., Gemmell, J.F., Horvath, M., Heimfarth, T.: Assessing distributed collaborative recommendations in different opportunistic network scenarios. Int. J. Grid Utility Comput. 11(5), 646–661 (2020)

    Article  Google Scholar 

  7. Bernaola, J.R.M., Sobrón, I., Del Ser, J., Landa, I., Eizmendi, I., Vélez, M.: Ensemble learning for seated people counting using wifi signals: performance study and transferability assessment. In: 2021 IEEE Globecom Workshops (GC Workshops), pp. 1–6. IEEE (2021)

  8. Brena, R.F., Escudero, E., Vargas-Rosales, C., Galvan-Tejada, C.E., Munoz, D.: Device-free crowd counting using multi-link Wi-Fi CSI descriptors in Doppler spectrum. Electronics 10(3), 315 (2021)

    Article  Google Scholar 

  9. Chaudhari, M.D., Ghotkar, A.S.: A study on crowd detection and density analysis for safety control. Int. J. Comput. Sci. Eng. 6, 424–428 (2018)

    Google Scholar 

  10. Choi, H., Fujimoto, M., Matsui, T., Misaki, S., Yasumoto, K.: Wi-cal: Wifi sensing and machine learning based device-free crowd counting and localization. IEEE Access 10, 24395–24410 (2022)

    Article  Google Scholar 

  11. Chrysler, A., Gunarso, R., Puteri, T., Warnars, H.: A literature review of crowd-counting system on convolutional neural network. In: IOP Conference Series: Earth and Environmental Science, vol. 729, p. 012029. IOP Publishing, Bristol (2021)

  12. Elharrouss, O., Almaadeed, N., Abualsaud, K., Al-Ali, A., Mohamed, A., Khattab, T., Al-Maadeed, S.: Drone-scnet: scaled cascade network for crowd counting on drone images. IEEE Trans. Aerosp. Electron. Syst. 57(6), 3988–4001 (2021)

    Article  ADS  Google Scholar 

  13. Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., Dovzhenko, A., Tietz, O., Bosco, C.D., Walsh, S., Saltukoglu, D., Tay, T.L., Prinz, M., Palme, K., Simons, M., Diester, I., Brox, T., Ronneberger, O.: U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2018)

    Article  PubMed  Google Scholar 

  14. Fan, Z., Zhang, H., Zhang, Z., Lu, G., Zhang, Y., Wang, Y.: A survey of crowd counting and density estimation based on convolutional neural network. Neurocomputing 472, 224–251 (2022)

    Article  Google Scholar 

  15. Gao, M., Jiang, J., Shen, J., Zou, G., Fu, G.: Crowd motion segmentation and behavior recognition fusing streak flow and collectiveness. Opt. Eng. 57(4), 043109 (2018)

    Article  ADS  Google Scholar 

  16. Gao, G., Gao, J., Liu, Q., Wang, Q., Wang, Y.: CNN-based density estimation and crowd counting: a survey. arXiv Preprint (2020). ARXIV:2003.12783

  17. Gu, C., Wang, C., Gao, B.B., Liu, J., Zhang, T.: HDNet: a hierarchically decoupled network for crowd counting. In: 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2022)

  18. Guo, X., Gao, M., Zhai, W., Li, Q., Pan, J., Zou, G.: Multiscale aggregation network via smooth inverse map for crowd counting. Multimedia Tools Appl. (2022). https://doi.org/10.1007/s11042-022-13664-8

  19. Guo, X., Gao, M., Zhai, W., Shang, J., Li, Q.: Spatial-frequency attention network for crowd counting. Big Data 10(5), 453–465 (2022)

    Article  PubMed  Google Scholar 

  20. Hassen, K.B.A., Machado, J.J., Tavares, J.M.R.: Convolutional neural networks and heuristic methods for crowd counting: a systematic review. Sensors 22(14), 5286 (2022)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  21. Huang, Q., Rodriguez, K., Whetstone, N., Habel, S.: Rapid internet of things (IoT) prototype for accurate people counting towards energy efficient buildings. J. Inf. Technol. Constr. 24, 1–13 (2019)

    CAS  Google Scholar 

  22. Ibrahim, O.T., Gomaa, W., Youssef, M.: Crosscount: a deep learning system for device-free human counting using WIFI. IEEE Sensors J. 19(21), 9921–9928 (2019)

    Article  ADS  Google Scholar 

  23. Ilyas, N., Shahzad, A., Kim, K.: Convolutional-neural network-based image crowd counting: Review, categorization, analysis, and performance evaluation. Sensors 20(1), 43 (2019)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  24. Jaswanth, V., Yeduguru, A.R., Manoj, V.S., Deepak, K., Chandrakala, S.: Recent trends and study on perspective crowd counting in smart environments. In: Artificial Intelligence and Technologies, pp. 63–72. Springer, Singapore (2022)

  25. Jiang, J., Zhuojun, Z., Mingliang, G., et al.: An abnormal crowd behavior detection method based on streak flow CNN. Adv. Eng. Sci. 52(6), 215–222 (2020)

    Google Scholar 

  26. Jiang, J., Wang, X., Gao, M., Pan, J., Zhao, C., Wang, J.: Abnormal behavior detection using streak flow acceleration. Appl. Intell. 52, 10632–10649 (2022)

    Article  Google Scholar 

  27. Khan, D., Ho, I.W.H.: Crosscount: efficient device-free crowd counting by leveraging transfer learning. IEEE Internet Things J. (2022). https://doi.org/10.1109/JIOT.2022.3171449

    Article  Google Scholar 

  28. Khan, A., Kadir, K.A., Nasir, H., Shah, J.A., Albattah, W., Khan, S., Kakakhel, M.H.: Crowd counting and localization beyond density map. IEEE Access (2022). https://doi.org/10.1109/ACCESS.2022.3224429

    Article  PubMed  Google Scholar 

  29. Kianoush, S., Savazzi, S., Rampa, V., Nicoli, M.: People counting by dense WIFI MIMO networks: channel features and machine learning algorithms. Sensors 19(16), 3450 (2019)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  30. Li, B., Huang, H., Zhang, A., Liu, P., Liu, C.: Approaches on crowd counting and density estimation: a review. Pattern Anal. Appl. 24(3), 853–874 (2021)

    Article  Google Scholar 

  31. Ling, M., Geng, X.: Indoor crowd counting by mixture of Gaussians label distribution learning. IEEE Trans. Image Process. 28(11), 5691–5701 (2019)

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  32. Liu, S., Zhao, Y., Xue, F., Chen, B., Chen, X.: Deepcount: crowd counting with WIFI via deep learning. arXiv preprint (2019). arXiv:1903.05316

  33. Liu, Z., Yuan, R., Yuan, Y., Yang, Y., Guan, X.: A sensor-free crowd counting framework for indoor environments based on channel state information. IEEE Sensors J. 22(6), 6062–6071 (2022)

    Article  ADS  Google Scholar 

  34. Metwaly, A., Queralta, J.P., Sarker, V.K., Gia, T.N., Nasir, O., Westerlund, T.: Edge computing with embedded ai: Thermal image analysis for occupancy estimation in intelligent buildings. In: Proceedings of the INTelligent Embedded Systems Architectures and Applications Workshop 2019, pp. 1–6 (2019)

  35. Nguyen, V., Ngo, T.D.: Single-image crowd counting: a comparative survey on deep learning-based approaches. Int. J. Multimedia Inf. Retrieval 9(2), 63–80 (2020)

    Article  MathSciNet  Google Scholar 

  36. Padmashini, M., Manjusha, R., Parameswaran, L.: Vision based algorithm for people counting using deep learning. Int. J. Eng. Technol. 7(3.6), 74 (2018)

    Article  Google Scholar 

  37. Ptak, B., Pieczyński, D., Piechocki, M., Kraft, M.: On-board crowd counting and density estimation using low altitude unmanned aerial vehicles-looking beyond beating the benchmark. Remote Sensing 14(10), 2288 (2022)

    Article  ADS  Google Scholar 

  38. Ramachandraarjunan, S., Perumalsamy, V., Narayanan, B.: IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques. J. Intell. Fuzzy Syst. (2022). https://doi.org/10.3233/jifs-212955

    Article  Google Scholar 

  39. Rizvi, S.Z., Farooq, M.U., Raza, R.H.: Performance comparison of deep residual networks-based super resolution algorithms using thermal images: case study of crowd counting. In: Conference on Multimedia, Interaction, Design and Innovation, pp. 75–87. Springer, Cham (2022)

  40. Rolt, C.R.D., Dias, J.D.S., Gomes, E.H., Buosi, M.: Crowdsensing campaigns management in smart cities. Int. J. Grid Utility Comput. 12(2), 192–204 (2021)

    Article  Google Scholar 

  41. Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Comput. Vis. Image Underst. 130, 1–17 (2015)

    Article  Google Scholar 

  42. Sindagi, V.A., Patel, V.M.: A survey of recent advances in CNN-based single image crowd counting and density estimation. Pattern Recogn. Lett. 107, 3–16 (2018)

    Article  ADS  Google Scholar 

  43. Sobron, I., Del Ser, J., Eizmendi, I., Vélez, M.: Device-free people counting in IoT environments: new insights, results, and open challenges. IEEE Internet Things J. 5(6), 4396–4408 (2018)

    Article  Google Scholar 

  44. Solmaz, G., Baranwal, P., Cirillo, F.: Countmein: Adaptive crowd estimation with wi-fi in smart cities. In: 2022 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 187–196. IEEE (2022)

  45. Souri, A., Chen, M.Y.: AI-enabled learning techniques for Internet of Things. J. High Speed Netw. 27(3), 203–204 (2021)

    Article  Google Scholar 

  46. Tang, C., Li, W., Vishwakarma, S., Chetty, K., Julier, S., Woodbridge, K.: Occupancy detection and people counting using WIFI passive radar. In: 2020 IEEE Radar Conference (RadarConf20), pp. 1–6. IEEE (2020)

  47. Terroso-Sáenz, F., Martínez-Guillén, D., Llanes, A.: An open source system for people counting based on 802.11 packets tracking. In: 2022 IEEE Symposium on Wireless Technology & Applications (ISWTA), pp. 41–46. IEEE (2022)

  48. Thangam, A.J., Siva, P.T., Yogameena, B.: Crowd count in low resolution surveillance video using head detector and color based segmentation for disaster management. In: 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 1905–1909. IEEE (2015)

  49. Tsou, P.R., Wu, C.E., Chen, Y.R., Ho, Y.T., Chang, J.K., Tsai, H.P.: Counting people by using convolutional neural network and a PIR array. In: 2020 21st IEEE International Conference on Mobile Data Management (MDM), pp. 342–347. IEEE (2020)

  50. Wang, F., Zhang, F., Wu, C., Wang, B., Liu, K.R.: Respiration tracking for people counting and recognition. IEEE Internet Things J. 7(6), 5233–5245 (2020)

    Article  Google Scholar 

  51. Wang, X., Chen, G., Gong, H., Jiang, J.: UAV swarm autonomous control based on Internet of Things and Artificial Intelligence algorithms. J. Intell. Fuzzy Syst. 40(4), 7121–7133 (2021)

    Article  Google Scholar 

  52. Wang, Z., Fan, J., Song, X., Zhou, N., Chen, F., Guo, Y., Chen, D.: Crowd counting based on CSI and convolutional neural network. In: 2021 33rd Chinese Control and Decision Conference (CCDC), pp. 1249–1254. IEEE (2021)

  53. Xie, W., Noble, J.A., Zisserman, A.: Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6, 283–292 (2018)

    Article  Google Scholar 

  54. Yang, Y., Cao, J., Liu, X., Liu, X.: Wi-count: passing people counting with cots WIFI devices. In: 2018 27th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9. IEEE (2018)

  55. Yu, Q., Hu, L., Alzahrani, B., Baranawi, A., Alhindi, A., Chen, M.: Intelligent visual-IoT-enabled real-time 3D visualization for autonomous crowd management. IEEE Wirel. Commun. 28(4), 34–41 (2021)

    Article  Google Scholar 

  56. Zhai, W., Gao, M., Anisetti, M., Li, Q., Jeon, S., Pan, J.: Group-split attention network for crowd counting. J. Electron. Imaging 31(4), 041214 (2022)

    Article  ADS  Google Scholar 

  57. Zhai, W., Gao, M., Souri, A., Li, Q., Guo, X., Shang, J., Zou, G.: An attentive hierarchy convnet for crowd counting in smart city. Cluster Comput. (2022). https://doi.org/10.1007/s10586-022-03749-2

    Article  Google Scholar 

  58. Zhai, W., Li, Q., Zhou, Y., Li, X., Pan, J., Zou, G., Gao, M.: Da2net: a dual attention-aware network for robust crowd counting. Multimedia Syst. (2022). https://doi.org/10.1007/s00530-021-00877-4

    Article  Google Scholar 

  59. Zhang, H., Zhou, M., Sun, H., Zhao, G., Qi, J., Wang, J., Esmaiel, H.: Que-Fi: a Wi-Fi deep-learning-based queuing people counting. IEEE Syst. J. 15(2), 2926–2937 (2020)

    Article  ADS  Google Scholar 

  60. Zhou, F., Zhao, H., Zhang, Y., Zhang, Q., Liang, L., Li, Y., Duan, Z.: COMAL: compositional multi-scale feature enhanced learning for crowd counting. Multimedia Tools Appl. 81, 20541–20560 (2022)

    Article  Google Scholar 

  61. Zou, H., Zhou, Y., Yang, J., Gu, W., Xie, L., Spanos, C.: Freecount: device-free crowd counting with commodity WIFI. In: GLOBECOM 2017-2017 IEEE Global Communications Conference, pp. 1–6. IEEE (2017)

  62. Zou, H., Zhou, Y., Yang, J., Spanos, C.J.: Device-free occupancy detection and crowd counting in smart buildings with WIFI-enabled IoT. Energy Build. 174, 309–322 (2018)

    Article  Google Scholar 

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Acknowledgements

This work is in part supported by the National Natural Science Foundation of China (Grant No. 61601266).

Funding

This work is in part supported by the National Natural Science Foundation of China (Grant No. 61601266).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, China

    Mingliang Gao, Alireza Souri, Wenzhe Zhai & Xiangyu Guo

  2. Department of Software Engineering, Haliç University, 34060, Istanbul, Turkey

    Alireza Souri

  3. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mayram Zaker

  4. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK

    Qilei Li

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  1. Mingliang Gao
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Correspondence to Alireza Souri.

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Gao, M., Souri, A., Zaker, M. et al. A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things. Cluster Comput 27, 859–873 (2024). https://doi.org/10.1007/s10586-023-03987-y

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