EgoQR: Efficient QR Code Reading in Egocentric Settings
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912062847844352 |
|---|---|
| author | Moslehpour, Mohsen Lu, Yichao Chuang, Pierce Shenoy, Ashish Chatterjee, Debojeet Harpale, Abhay Jayakumar, Srihari Bhardwaj, Vikas Nam, Seonghyeon Kumar, Anuj |
| author_facet | Moslehpour, Mohsen Lu, Yichao Chuang, Pierce Shenoy, Ashish Chatterjee, Debojeet Harpale, Abhay Jayakumar, Srihari Bhardwaj, Vikas Nam, Seonghyeon Kumar, Anuj |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EgoQR: Efficient QR Code Reading in Egocentric Settings Moslehpour, Mohsen Lu, Yichao Chuang, Pierce Shenoy, Ashish Chatterjee, Debojeet Harpale, Abhay Jayakumar, Srihari Bhardwaj, Vikas Nam, Seonghyeon Kumar, Anuj Computer Vision and Pattern Recognition 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. |
| title | EgoQR: Efficient QR Code Reading in Egocentric Settings |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.05497 |