Abstract
<p>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.</p>
Highlights
Adequate safety stock levels permit business operations to proceed according to their plans
This paper proposed a smart solution for out of stock issue in warehouses, by using computer vision technology
The obtained results will be discussed from conducting our own real time experiments
Summary
Adequate safety stock levels permit business operations to proceed according to their plans. Products usually have either an RFID tag or barcode label, so they can be scanned and identified by the system [3] This is how systems can provide visibility into inventory levels, expiration dates, item location, forecast demand, and more [4]. Convolutional Neural Network (CNN) was used in real time mentoring for inventory management [5] This aims to have an efficient method to count and localize the objects in inventory by utilizing computer vision technology. An information system to detect ( measure) products in grocery retail sector for example, which are not on the shelf, is made possible [6] This has come up with a technical solution for stockout risk too. The system was able to detect about 27% of the daily occurring OOS cases with accuracy greater than 90%
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