EgoQR: Efficient QR Code Reading in Egocentric Settings
Mohsen Moslehpour, Yichao Lu, Pierce Chuang, Ashish Shenoy, Debojeet, Chatterjee, Abhay Harpale, Srihari Jayakumar, Vikas Bhardwaj, Seonghyeon Nam,, Anuj Kumar
TL;DR
EgoQR is a specialized system designed for efficient and accurate QR code reading from egocentric images captured by wearable devices, addressing unique challenges like wide field-of-view and resource constraints.
Contribution
We introduce EgoQR, a novel detection and decoding system optimized for egocentric images on wearable devices, with innovative techniques for perspective and motion challenges.
Findings
34% improvement over existing QR code readers
Operates efficiently on high-resolution images with minimal power
Handles wide field-of-view and motion blur effectively
Abstract
QR codes have become ubiquitous in daily life, enabling rapid information exchange. With the increasing adoption of smart wearable devices, there is a need for efficient, and friction-less QR code reading capabilities from Egocentric point-of-views. However, adapting existing phone-based QR code readers to egocentric images poses significant challenges. Code reading from egocentric images bring unique challenges such as wide field-of-view, code distortion and lack of visual feedback as compared to phones where users can adjust the position and framing. Furthermore, wearable devices impose constraints on resources like compute, power and memory. To address these challenges, we present EgoQR, a novel system for reading QR codes from egocentric images, and is well suited for deployment on wearable devices. Our approach consists of two primary components: detection and decoding, designed to…
Peer Reviews
Decision·Submitted to ICLR 2025
1.complex system implementation 2.great practical significance 3.experimental results in real-world environments
1.The method lacks novelty 2.The method is a simple combination of existing modules
S1. The proposed method resolves stylistic QR code and small QR code problems by utilizing image enhancement modules, such as color inversion, multi-scale processing, contrast enhancement morphological operation, and super-resolution.
W1. Novelty. * The paper proposes a simple pipeline that combines a QR code detection module, an image enhancement module, and a QR code decoding module. It is difficult to find any new idea, novelty, or new perspective for each component. ICLR requires new and brilliant ideas, perspectives, and contributions to various research fields. However, the paper's contribution is not enough to meet ICLR standards. W2. Presentation * The paper presentation needs to be clarified. For instance, Table 2
- The proposed EgoQR system demonstrated a significant 34% improvement in QR code reading success over existing state-of-the-art readers. - By incorporating image enhancement techniques like super-resolution and adaptive histogram equalization, the system improves decoding success rates even in challenging conditions such as motion blur and varying lighting.
- Lack of novelty: The paper builds upon well-established techniques such as Faster R-CNN for detection and common image enhancement methods for decoding. - Concern about efficiency: The proposed system employs high-resolution image processing and machine learning models which could be computationally intensive. It would be beneficial to see a more detailed analysis of the trade-offs between performance and resource consumption.
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Taxonomy
TopicsQR Code Applications and Technologies · Caching and Content Delivery · IPv6, Mobility, Handover, Networks, Security