Tracking objects using QR codes and deep learning

Ali Ahmadinia; Atika Singh; Kambiz Hamadani; Yuanyuan Jiang

doi:10.1117/12.2679910

7 June 2023 Tracking objects using QR codes and deep learning

Ali Ahmadinia, Atika Singh, Kambiz Hamadani, Yuanyuan Jiang

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 1270106 (2023) https://doi.org/10.1117/12.2679910
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

Despite recent advances in deep learning, object detection and tracking still require considerable manual and computational effort. First, we need to collect and create a database of hundreds or thousands of images of the target objects. Next we must annotate or curate the images to indicate the presence and position of the target objects within those images. Finally, we must train a CNN (convolution neural network) model to detect and locate the target objects in new images. This training is usually computationally intensive, consists of thousands of epochs, and can take tens of hours for each target object. Even after the model training in completed, there is still a chance of failure if the real-time tracking and object detection phases lack sufficient accuracy, precision, and/or speed for many important applications. Here we present a system and approach which minimizes the computational expense of the various steps in the training and real-time tracking process outlined above of for applications in the development of mixed-reality science laboratory experiences by using non-intrusive object-encoding 2D QR codes that are mounted directly onto the surfaces of the lab tools to be tracked. This system can start detecting and tracking it immediately and eliminates the laborious process of acquiring and annotating a new training dataset for every new lab tool to be tracked.

Citation Download Citation

Ali Ahmadinia, Atika Singh, Kambiz Hamadani, and Yuanyuan Jiang "Tracking objects using QR codes and deep learning", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 1270106 (7 June 2023); https://doi.org/10.1117/12.2679910

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Education and training

Object detection

Data modeling

Deep learning

Detection and tracking algorithms

Image processing

Calibration

Show All Keywords

Keywords/Phrases

Search In:

Publication Years