AI Video Enhancement for WMS

Author(s)	B. Bala Krishna, S. Ranitha, B. Akhila, S. Roopa Chandana
Country	India
Abstract	Warehouse Management Systems (WMS) rely on video surveillance to keep an eye on worker activities, inventory movement, and operational safety. However, low resolution, motion blur, missing frames, and poor lighting are common issues with warehouse video feeds that impair monitoring accuracy and influence decision-making. In order to improve the quality of warehouse surveillance footage in real time, this paper suggests an AI-based video enhancement framework. Using Convolutional Neural Networks (CNNs), the system uses deep learning techniques like frame interpolation, super-resolution, and noise reduction. To increase operational efficiency and automate inventory updates, the processed data is integrated with the warehouse management system. When compared to conventional video processing methods, experimental evaluation shows notable gains in visual clarity and detection accuracy. In contemporary warehouse settings, the suggested framework facilitates intelligent decision-making, improves safety, and lessens the need for manual monitoring.
Keywords	Artificial Intelligence,Object Detection,Conventional Neural Networks (CNN), Frame Interpolation,Warehouse Management System.
Discipline	Engineering
Published In	Volume 4, Issue 2, March-April 2026
Published On	2026-04-06
DOI	https://doi.org/10.62127/aijmr.2026.v04i02.1273

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