Fast Batch Reading Densely Deployed QR Codes

This paper presents BatchQR, a mobile APP that can batch read densely arranged QR codes attached to caps of the tubes and vials in clinical and biological labs. BatchQR could work in two modes: photo mode and preview mode. For the photo mode, we first propose an IFFT based lightweight code detection mechanism, which can adaptively adjust operating parameters to identify densely arranged QR codes in practice. We propose an image refocus mechanism to deal with blurs/distorts that may appear in the photo, and a lightweight learning based classifier to filter out falsely detected QR codes. We further optimize BatchQR by enabling batch reading in the preview mode of the camera, which is more in line with common usage habits. To this end, we develop a QR code tracking algorithm based on Kalman filtering, which keeps track of each code image dynamically. We also design a parallel acceleration mechanism based on multi-core CPU and GPU, which significantly mitigates the end-to-end delay. Comprehensive experimental results show that BatchQR can read up to 160 Version 1-H QR codes in one shot, with 95% accuracy and 100-400ms delay, which is only 0.1% of the time consumed by the regular QR decoder.