Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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 presented, a novel system for reading QR codes from egocentric images, designed to operate on high-resolution images on the device with minimal power consumption and added latency, and is well suited for deployment on wearable devices.

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 operate on high-resolution images on the device with minimal power consumption and added latency. The detection component efficiently locates potential QR codes within the image, while our enhanced decoding component extracts and interprets the encoded information. We incorporate innovative techniques to handle the specific challenges of egocentric imagery, such as varying perspectives, wider field of view, and motion blur. We evaluate our approach on a dataset of egocentric images, demonstrating 34% improvement in reading the code compared to an existing state of the art QR code readers.

EgoQR: Efficient QR Code Reading in Egocentric Settings

TL;DR

EgoQR is presented, a novel system for reading QR codes from egocentric images, designed to operate on high-resolution images on the device with minimal power consumption and added latency, and is well suited for deployment on wearable devices.

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 operate on high-resolution images on the device with minimal power consumption and added latency. The detection component efficiently locates potential QR codes within the image, while our enhanced decoding component extracts and interprets the encoded information. We incorporate innovative techniques to handle the specific challenges of egocentric imagery, such as varying perspectives, wider field of view, and motion blur. We evaluate our approach on a dataset of egocentric images, demonstrating 34% improvement in reading the code compared to an existing state of the art QR code readers.
Paper Structure (16 sections, 1 equation, 8 figures, 2 tables)

This paper contains 16 sections, 1 equation, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Previous works
  3. Architecture
  4. Detection
  5. Decoding
  6. Super Resolution
  7. Fulfillment
  8. Handling Disambiguation
  9. Handling Reading Errors
  10. Experimental Results
  11. Experimental Setup
  12. Benchmark Dataset
  13. Metrics
  14. Experimental Results
  15. Comparative Analysis
  16. ...and 1 more sections

Figures (8)

  • Figure 1: Overall architecture of the code reading flow from the phone taken from user's command to final fulfilment.
  • Figure 2: QR code detection examples
  • Figure 3: Super resolution input (left: not decodable) and output (right: decodable).
  • Figure 4: Examples of QR Code Disambiguation.
  • Figure 5: Examples of QR Code Reading Errors.
  • ...and 3 more figures