SMARTSTOCK: Real-Time AI-Powered Vision System for Automated Warehouse Inventory Management

Authors

Lum Fu Yuan

Faculty of Artificial Intelligence and Cyber Security, University Technical Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100 (Malaysia)

Yun-Huoy Choo

Faculty of Artificial Intelligence and Cyber Security, University Technical Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100 (Malaysia)

Azah Kamilah Muda

Faculty of Artificial Intelligence and Cyber Security, University Technical Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100 (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2025.91100289

Subject Category: Artificial Intelligence

Volume/Issue: 9/11 | Page No: 3714-3727

Publication Timeline

Submitted: 2025-11-12

Accepted: 2025-11-18

Published: 2025-12-08

Abstract

SMARTSTOCK is an intelligent warehouse management system developed to overcome long-standing challenges in stock visibility, manual inventory counting, and inefficient resource utilization. The system integrates artificial intelligence, computer vision, and image processing to enable real-time stock detection, customer presence tracking, and parking lot traffic monitoring. Designed to enhance operational transparency and decision-making efficiency, SMARTSTOCK automates key warehouse functions traditionally dependent on manual supervision. The architecture comprises two core modules: an AI-based Video Recognition Module and a Real-Time Visibility and Reporting Module. The AI-based Video Recognition Module incorporates three sub-modules—Stock Detection and Tracking, Client Presence Counting, and Car Park Counting—which employ YOLOv8 models for object detection and the SORT algorithm for object tracking and counting. Experimental evaluation demonstrated high reliability and accuracy, achieving 90.16% for stock tracking, 95% for client presence counting, and 100% for car park occupancy detection. The Real-Time Visibility and Reporting Module provides a unified dashboard for data visualization, live monitoring, and decision support, significantly reducing human error and out-of-stock occurrences. Despite its strong performance, SMARTSTOCK faces limitations related to hardware dependency and difficulty in detecting low-stock items under full occlusion. Future enhancements will focus on cloud-based implementation, model optimization, and integration with point-of-sale systems to achieve comprehensive inventory intelligence. Overall, SMARTSTOCK represents a robust, explainable, and scalable AI-driven framework that advances warehouse automation, improves resource utilization, and strengthens real-time decision-making within retail environments.

Keywords

Warehouse Automation, Computer Vision Object Tracking

Downloads

References

1. Gu, J., & Liu, F. (2022). A literature review of smart warehouse operations management. Frontiers of Engineering Management, 9, 31–55. [Google Scholar] [Crossref]

2. Budiyanto, A., & Muslim, M. (2024). Optimizing Inventory Systems with RFID: A Narrative Review of Integration, Efficiency, and Barriers. Sinergi International Journal of Logistics, 2(2), 133‑146. [Google Scholar] [Crossref]

3. Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022). Detection and Tracking Meet Drones challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7380–7399. [Google Scholar] [Crossref]

4. Tepteris, G., Mamasis, K., & Minis, I. (2025). Logistics Hub Surveillance: Optimizing YOLOv3 Training for AI-Powered Drone Systems. Logistics, 9(2), 45. [Google Scholar] [Crossref]

5. Koteswara Rao, M., & Ashok Kumar, P. M. (2025). Advanced Object Tracking in Video Surveillance Systems with Adaptive Deep SORT Enhancement. Engineering, Technology & Applied Science Research, 15(2), 20871–20877. [Google Scholar] [Crossref]

6. Li, Y., Qu, L., Cai, G., Cheng, G., Qian, L., Dou, Y., ... & Wang, S. (2023). Video Object Counting with Scene-Aware Multi-Object Tracking. Journal of Database Management (JDM), 34(3), 1-13. [Google Scholar] [Crossref]

7. Maurya, A. S. (2023). Vehicle Detection in Autonomous Vehicles Using Yolov8. International Research Journal of Modernization in Engineering Technology and Science, 5(10), 3108-3113. [Google Scholar] [Crossref]

8. Mandal, V., & Adu-Gyamfi, Y. (2020). Object detection and tracking algorithms for vehicle counting: a comparative analysis. Journal of big data analytics in transportation, 2(3), 251-261. [Google Scholar] [Crossref]

9. Li, Y., Qu, L., Cai, G., Cheng, G., Qian, L., Dou, Y., ... & Wang, S. (2023). Video Object Counting with Scene-Aware Multi-Object Tracking. Journal of Database Management (JDM), 34(3), 1-13. [Google Scholar] [Crossref]

10. Zhang, Y., Li, L., & Qu, L. (2020). Video object counting with scene-aware multi-object tracking. IGI Global. [Google Scholar] [Crossref]

11. Ren, P., Fang, W., & Djahel, S. (2017, September). A novel YOLO-Based real-time people counting approach. In 2017 international smart cities conference (ISC2) (pp. 1-2). IEEE. [Google Scholar] [Crossref]

12. Fudholi, D. H., Kurniawardhani, A., Andaru, G. I., Alhanafi, A. A., & Najmudin, N. (2024). YOLO-based small-scaled model for On-Shelf availability in retail. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(2), 265–271. [Google Scholar] [Crossref]

13. Balasubramanian, S., & Feizi, S. (2023). Towards improved input masking for convolutional neural networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1855–1865). [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles