A real-time mobile notification system for inventory stock out detection using SIFT and RANSAC

• Main Authors: Merrad, Yacine, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Gunawan, Teddy Surya
• Format: Article
• Language: English; English
• Published: International Association of Online Engineering Austria 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://irep.iium.edu.my/80069/1/13315-45277-1-PB.pdf; http://irep.iium.edu.my/80069/7/80069_A%20real-time%20mobile%20notification%20system%20for%20inventory%20stock%20out_SCOPUS.pdf; http://irep.iium.edu.my/80069/; https://www.online-journals.org/index.php/i-jim/article/view/13315/6763
• Institution: Universiti Islam Antarabangsa Malaysia

Object detection and tracking is one of the most relevant computer technologies related to computer vision and image processing. It may mean the detection of an object within a frame and classify it (human, animal, vehicle, building, etc) by the use of some algorithms. It may also be the detection of a reference object within different frames (under different angles, different scales, etc.). The applications of the object detection and tracking are numerous; most of them are in the security field. It is also used in our daily life applications, especially in developing and enhancing business management. Inventory or stock management is one of these applications. It is considered to be an important process in warehousing and storage business because it allows for stock in and stock out products control. The stock-out situation, however, is a very serious issue that can be detrimental to the bottom line of any business. It causes an increased risk of lost sales as well as it leads to reduced customer satisfaction and lowered loyalty levels. On this note, a smart solution for stock-out detection in warehouses is proposed in this paper, to automate the process using inventory management software. The proposed method is a machine learning based real-time notification system using the exciting Scale Invariant Feature Transform feature detector (SIFT) and Random Sample Consensus (RANSAC) algorithms. Consequently, the comparative study shows the overall good performance of the system achieving 100% detection accuracy with features’ rich model and 90% detection accuracy with features’ poor model, indicating the viability of the proposed solution.