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Updated: Apr 28, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

On-device mobile visual location recognition by using panoramic images and compressed sensing based visual

Tao Guan¹, Yin Fan¹, Liya Duan¹

¹School of Computer Science and Technology, Huazhong University of Science & Technology, Wuhan, People's Republic of China.

|June 4, 2014

Summary

This study introduces a novel on-device mobile visual location recognition system using panoramic images. It enhances accuracy by employing a heading-aware Bag-of-Features model and efficient bilinear compressed sensing for mobile deployment.

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Area of Science:

Computer Vision
Mobile Computing
Robotics

Background:

Mobile Visual Location Recognition (MVLR) commonly uses Query-by-Example (QBE), which struggles with variations in capture conditions and viewpoints.
Existing QBE-based MVLR systems lack reliability for on-device applications due to these limitations.

Purpose of the Study:

To design a robust panorama-based on-device MVLR system.
To improve the reliability and efficiency of MVLR for mobile platforms.

Main Methods:

Developed a heading-aware Bag-of-Features (BOF) model for panoramic image descriptors, incorporating digital compass data.
Proposed a bilinear compressed sensing based encoding method for efficient searching of high-dimensional visual descriptors on mobile devices.
Released a new benchmark dataset comprising panoramic images and queries for MVLR research.

Main Results:

The heading-aware BOF model effectively generates descriptors suitable for panoramic images.
The bilinear compressed sensing method enables fast, accurate, and memory-efficient on-device visual descriptor searching.
Experimental results demonstrate the proposed methods' effectiveness for on-device MVLR.

Conclusions:

The developed system significantly enhances the performance of on-device MVLR.
The proposed methods address key challenges in visual location recognition on mobile devices.
The new benchmark dataset will foster further advancements in the field of MVLR.