We consider the visual barcode recognition problem in a noisy video data setup. Unlike most existing single-frame recognizers that require considerable user effort to acquire clean, motionless and blur-free barcode signals, we eliminate such extra human efforts by proposing a robust video-based barcode recognition algorithm. We deal with a sequence of noisy blurred barcode image frames by posing it as an online filtering problem. In the proposed dynamic recognition model, at each frame we infer the blur level of the frame as well as the digit class label. In contrast to a frame-by-frame based approach with heuristic majority voting scheme, the class labels and frame-wise noise levels are propagated along the frame sequences in our model, and hence we exploit all cues from noisy frames that are potentially useful for predicting the barcode label in a probabilistically reasonable sense. We also suggest a visual barcode tracking approach that efficiently localizes barcode areas in video frames. The effectiveness of the proposed approaches is demonstrated empirically on both synthetic and real data setup.

Keywords: Hidden Markov models,Online sequential filtering,Barcode recognition

It is worth noting that the in-plane rotations were already dealt with in the previous experiments. However, the out-of-plane rotations, if significant changes, may not be properly handled by the proposed approach since we use a simple equal division to extract each digit code. So, we collect the videos with mild changes in out-of-plane pose changes.

No potential conflict of interest relevant to this article was reported.