Automatic extraction of 1D barcodes from video scans for drone-assisted inventory management in warehousing applications

The widespread use of barcodes has significantly contributed to accurate, efficient and economic inventory management in warehouses and distribution centres. However, its efficiency has always been limited by the primary method of reading barcodes with a handheld laser scanner. Compared with this reading by line-of-sight at close proximity, vision-based barcode reading algorithms can further improve efficiency, particularly if accompanied by automated data collection platforms such as drones. This paper introduces algorithms that are able to automatically extract barcodes from video data, and verifies their feasibility and promise for inventory management in warehousing applications. Three key techniques corresponding to different recognition levels are proposed: For a known barcode region, a Harris corner detector and Hough transform-based algorithm is applied to quickly estimate the angle by which the frame area needs to rotate to orient the bars vertically for information extraction. Then, the idea of exploiting connectivity and geometry property of barcode areas is proposed to directly recognise multiple barcode regions in a single video frame to eliminate reading difficulties resulting from interactive influence of multiple juxtaposed barcodes, and to save computation time by only processing frame areas of interest for valid barcodes. In addition, a histogram difference-based fast extraction strategy is designed to further improve efficiency by reducing duplicate information processing. Finally, the performance of each technique is evaluated by analysing video data from a large logistics warehouse, demonstrating satisfactory performance in inventory management applications.