Video Analytics for Optimizing Bank Services

Dhyey Valera*, Amit Vishwakarma**, Ananya Paliwal***, Veena Kulkarni****
*-**** Department of Computer Engineering, Thakur College of Engineering & Technology, Mumbai, India.
Periodicity:January - June'2023
DOI : https://doi.org/10.26634/jds.1.1.19480

Abstract

The use of Closed-Circuit Television (CCTV) footage for surveillance, demographic monitoring, and behavior analysis has increased dramatically in the recent years. One of the key challenges in analyzing such recordings is to count people and identify emotions efficiently and accurately. This paper proposed a system for counting people and identifying facial expressions of emotion in CCTV footage. It used Deep Neural Networks (DNN) trained on massive annotation datasets to recognize people in videos, extract facial images, and identify emotions. It evaluated the performance of the method on publicly available datasets and compares it with state-of-the-art techniques. The findings demonstrate the ability of the method to accurately count people and identify their emotions.

Keywords

Deep Learning, Face Recognition, Video Analytics, YOLO, CNN.

How to Cite this Article?

Valera, D., Vishwakarma, A., Paliwal, A., and Kulkarni, V. (2023). Video Analytics for Optimizing Bank Services. i-manager’s Journal on Data Science & Big Data Analytics, 1(1), 1-8. https://doi.org/10.26634/jds.1.1.19480

References

[2]. Dumas, M. (2001). Emotional expression recognition using support vector machines. In Proceedings of International Conference on Multimodal Interfaces.
[7]. Suhane, A. K., Vani, A., Parihar, H., Raghuwanshi, U., Nimbark, A., & Saxena, L. (2023). Human detection and crowd counting using yolo. In 2023 Tech Fest IIT Conference, Roorkee (pp. 1-7).
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 40 40 300
Online 40 40 300
Pdf & Online 40 40 300

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.