Journal of Applied Information Science

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Web-Based Application for Monitoring Crowd Status in Stores


Affiliations
1 Department of Computer Science & Engineering, Adi Shankara Institute of Engineering and Technology, Kerala, India
     

   Subscribe/Renew Journal


Crowd counting and surveillance in public spaces and stores is a difficult task to complete. Because of the large population and variety of human activities, crowded scenes must be more frequent. The requirement for a mechanism to track crowd counting in certain periods, such as during the COVID outbreak, is critical. In this COVID scenario, large crowds without keeping social distance and without any measures can easily spread the COVID-19 virus. Store personnels may find it difficult to handle large crowds when a large group of people gathers at a store to make a purchase. This study proposes a method that can be used in stores to assist customers to plan their shopping trips properly, maintaining social distance. This software primarily aids users in locating shops in their area that they wish to visit, as well as offering information on the quietest and busiest times of the day to visit each shop. The user interface of the system is designed in such a way that the search function and GPS function aid you in identifying nearby retailers as well as monitoring the state of local establishments to determine if they are crowded or not.

Keywords

Counting, Crowd, Distance, GPS, Retailers, Shopping, Store, Surveillance, Visit
Subscription Login to verify subscription
User
Notifications
Font Size


  • I. Culjak, D. Abram, T. Pribanic, H. Dzapo, and M. Cifrek, ‘‘A brief introduction to OpenCV,’’ in Proc. 35th Int. Conv. MIPRO, 2012, pp. 1725-1730.

  • T. Ko, ‘‘A survey on behavior analysis in video surveillance for homeland security applications,’’ in Proc. 37th IEEE Appl. Imag. Pattern Recognit. Workshop, Oct. 2008, pp. 1-8.

  • A. Hampapur, L. Brown, J. Connell, A. Ekin, N. Haas, M. Lu, H. Merkl, and S. Pankanti, ‘‘Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking,’’ IEEE Signal Process. Mag., vol. 22, no. 2, pp. 38-51, Mar. 2005.

  • E. Grevelink, ‘‘A closer look at object detection, recognition and tracking,’’ Intel, Santa Clara, CA, USA, Tech. Rep., 2017. [Online]. Available: https://software.intel.com/en-us/articles/a-closerlook-at-object-det ection-recognition-and-tracking

  • Z. Li, K. Ishibashi, K. Takai, and K. Yada, “Shop area visit ratio, stay time, and sales outcomes: In-depth analysis based on RFID data,” 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 2015, pp. 1-7, doi: 10.1109/APWCCSE.2015.7476231.

  • H. Phillips, and R. Bradshaw, “How customers actually shop: customer interaction with the point of sale,” Market Research Society Journal, vol. 35, no. 1, pp. 1-10, 1993, doi: 10.1177/147078539303500104.

  • N. Farooqi, “Intelligent safety management system for crowds using sensors,” 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), 2017, pp. 144-147, doi: 10.23919/ICITST.2017.8356365.

  • S. Rajarajeshwari, S. Archana, S. R. Boda, R. B. Sreelakshmi, K. Venkateswaran, and N. Sridhar, “A smart image processing system for hall management including social distancing - “SoDisCop”,” 2020 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020, pp. 1213-1219, doi: 10.1109/ICSSIT48917.2020.9214262.

  • K. H. Cheong et al., “Practical automated video analytics for crowd monitoring and counting,” in IEEE Access, vol. 7, pp. 183252-183261, 2019, doi: 10.1109/ACCESS.2019.2958255.

  • V. B. Subburaman, A. Descamps, and C. Carincotte, “Counting people in the crowd using a generic head detector,” 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, 2012, pp. 470-475, doi: 10.1109/AVSS.2012.87.


Abstract Views: 124

PDF Views: 0




  • A Web-Based Application for Monitoring Crowd Status in Stores

Abstract Views: 124  |  PDF Views: 0

Authors

Salbin Sadik
Department of Computer Science & Engineering, Adi Shankara Institute of Engineering and Technology, Kerala, India
V. Tushar Balakrishnan
Department of Computer Science & Engineering, Adi Shankara Institute of Engineering and Technology, Kerala, India
M. R. Vinayak
Department of Computer Science & Engineering, Adi Shankara Institute of Engineering and Technology, Kerala, India
B. Jain Stoble
Department of Computer Science & Engineering, Adi Shankara Institute of Engineering and Technology, Kerala, India

Abstract


Crowd counting and surveillance in public spaces and stores is a difficult task to complete. Because of the large population and variety of human activities, crowded scenes must be more frequent. The requirement for a mechanism to track crowd counting in certain periods, such as during the COVID outbreak, is critical. In this COVID scenario, large crowds without keeping social distance and without any measures can easily spread the COVID-19 virus. Store personnels may find it difficult to handle large crowds when a large group of people gathers at a store to make a purchase. This study proposes a method that can be used in stores to assist customers to plan their shopping trips properly, maintaining social distance. This software primarily aids users in locating shops in their area that they wish to visit, as well as offering information on the quietest and busiest times of the day to visit each shop. The user interface of the system is designed in such a way that the search function and GPS function aid you in identifying nearby retailers as well as monitoring the state of local establishments to determine if they are crowded or not.

Keywords


Counting, Crowd, Distance, GPS, Retailers, Shopping, Store, Surveillance, Visit

References