Article

Received 11.03.2023

Revised 29.07.2023

Accepted 28.08.2023

Retrieved from Vol. 27, No. 2, 2023

Pages 131 -140

Kharytonova, L., Shumeiko, O., Donets, V., & Kovalchuk, O. (2023). Innovative marketing technologies in retail using Computer Vision and artificial intelligence. The National Transport University Bulletin, 27(2), 131-140. https://doi.org/ 10.33744/2308-6645-2023-2-56-131-140

Innovative marketing technologies in retail using Computer Vision and artificial intelligence

Lesia Kharytonova Oleksii Shumeiko Veronika Donets Oksana Kovalchuk

The article discusses promising approaches to marketing research using computer vision technologies and video analytics based on artificial intelligence systems. The subject of the study is the processes of automated marketing research. The object of the research is technologies and means of automated analysis of the behavior of customers of retail establishments. The purpose of the work is to describe the concept and principles of automated systems for analyzing customer behavior in retail establishments using video surveillance systems, computer vision, and video analytics using artificial intelligence technology. In today's economic conditions, the issue of optimization and automation of all business processes of entities conducting economic activity, including marketing research processes in retail establishments, whose activities are characterized by a high degree of competition, has become extremely important. Automation of processes with the help of computer technologies and digital hardware allows to improve the quality of analytics and reduce research costs. The article is devoted to solving the issues of automating the research of the retail target audience using computer vision systems with the subsequent processing of these data using computer analytics systems using artificial intelligence

retail trade; marketing; innovative technologies; computer vision; pattern recognition; artificial intelligence
  1. Kreiman, G. Biological and Computer Vision. Cambridge University Press, 2021.
  2. Davies, E. R., Turk, M. A. Advanced Methods and Deep Learning in Computer Vision. Elsevier Academic Press, 2022.
  3. Howse, J., Minichino, J. Learning OpenCV 4 Computer Vision with Python 3. Third Edition. Packt Publishing, 2020.
  4. Koul, A., Ganju, S., & Kasatn, M. Practical Deep Learning for Cloud, Mobile and Edge Real-World AI and Computer-Vision Projects Using Python, Keras, and TensorFlow. O'Reilly, 2020.
  5. Alia, I., Duab, M. Smile Detection Using Data Amalgamation. Procedia Computer Science, 2020. Vol. 167, P. 979–986.
  6. Youngkyoon, J., Hatice G., & Ioannis, P. SmileNet: Registration-Free Smiling Face Detection In The Wild. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). P. 1581–1589, 2017.
  7. Ranjan, R., Sankaranarayanan S., Castillo, C., & Chellappa, R. An All-In-One Convolutional Neural Network for Face Analysis. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC. P. 17–24.
  8. Arriaga, O., Valdenegro-Toro, M., Plöger, P. Real-time Convolutional Neural Networks for Emotion and Gender Classification. [Electronic resource]. Available at: https://arxiv.org/abs/1710.07557. DOI: https://doi.org/10.48550/arXiv.1710.07557.
  9. Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV. Available at: https://github.com/oarriaga/face_classification/.
  10. Facial Expression Recognition Challenge. Deeplearning. Available at: http://deeplearning.net/icml2013-workshop-competition/challenges/.
  11. Facial Expression Recognition with a Deep Neural Network as a PyPI Package. Available at: https://github.com/oarriaga/face_classification/.
  12. Hierarchical Perception Library in Python for Pose Estimation, Object Detection, Instance Segmentation, Keypoint Estimation, Face Recognition, etc. Available at: https://github.com/oarriaga/paz.
  13. Creating a Heatmap Based on Video Recordings. Available at: https://github.com/kerberos-io/heatmap.
  14. Heatmap Using Yolov7 and DeepSORT. Available at: https://github.com/DoganK01/YOLOV7-DeepSORTRetail-Heat-Maps---Heatmaps-Density.
  15. Bailey, K. Retail Shopper Journey And Heat Map Analytics. Available at: https://www.aboutinsider.com/retail-shopper-journey-and-heat-map-analytics/ (accessed: June 21, 2022).

https://doi.org/ 10.33744/2308-6645-2023-2-56-131-140