## 1 Introduction  
This paper is an extension of the work originally presented in [1]. We extend our previous work from images of two viewpoints to the video frame. Furthermore, we propose a novel method for improving the accuracy of estimating the 3D position of inventory in the video frame. Finally, we validate the proposed framework using our generated datasets. In recent years, drones have become common throughout the world, and have been used in various fields [2]. Efficient routing with drones was proposed to minimize the time to serve all customers respecting all priority constraints [3]. The prevalence of drones has reduced the risk of accidents in hazardous work environments, and has enabled extensive exploration and long-term monitoring [4]. The traceability and monitoring of inventory in warehouses, one of the areas where drones are utilized, are essential factors in the durability of a company [5]. Several studies on drone-based inventory management have been conducted to improve the efficiency of inventory management [6, 7]. Recently, as the performance of deep learning models has improved, vision-based inventory management research using a drone-mounted monocular camera has been conducted. The vision-based position estimation technology is divided into feature point-based position estimation in the environment and marker-based position estimation [8]. Featurepoint-based position estimation has the advantage of being able to be used without additional work, such as attaching markers for application to the environment. However, its limitations are that complex operations for feature point recognition are required and that the application is limited in spaces with fewer feature points [9]. On the other hand, marker-based position estimation is not significantly affected by spatial characteristics. Recently, the drone-based inventory management automation method using the recognition of markers attached to inventory, such as barcodes and quick response codes (QR codes), has been proposed. Markers attached to shelves, floors, and walls guide the current position and movement path of a drone. For the inventory management of the warehouse, the drone moves along the guided route and scans the markers attached to the inventory. In the case of an indoor warehouse, the storage area of the inventory is clearly divided in the 3D space using shelves and grids. The position of the marker attached to the inventory may be specified using the information of the markers attached to shelves and grids in the same frame. However, in a storage yard without shelves and grids, the storage area of the inventory is not clearly classified. In other words, in a storage yard, the storage area of the inventory is flexible. In particular, it is difficult to obtain the height of the location where the inventory is stored in a warehouse without shelves. Most often, inventory management studies using drones have been conducted in warehouses with shelves and grids [6; 7]. Therefore, 3D positional estimation of inventory, including height, is necessary for inventory management automation in a storage yard. The inventory 3D position should be able to be estimated using the video of the warehouse or storage yard taken by a drone along the path of movement. Therefore, a method for 3D position estimation in the video frame is required. To solve this, a framework is proposed for 3D position estimation of the inventory QR code attached to the inventory in the video frame, using the ground QR code attached to the floor and the drone-mounted monocular camera, as shown in Figure 1. The proposed framework does not require shelves and grids for 3D position estimation of inventory. Furthermore, if drones could capture the QR code regardless of the presence of shelves and grids, the proposed framework is available in warehouses or storage yards with and without shelves and grids. Furthermore, a method for error correction of the estimated inventory 3D position in the video frame is proposed.