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<!DOCTYPE html>
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<meta name="description" content="A multiple-object tracker and dataset for infield cotton boll counting.">
<meta name="keywords" content="computer vision, multi-object tracking, agriculture, cotton">
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<title>NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting</title>
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<h1 class="title is-1 publication-title">NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting</h1>
<div class="is-size-5 publication-authors">
<span class="author-block"> Md Ahmed Al Muzaddid and <a href="https://ranger.uta.edu/~wjbeksi">William J. Beksi</a></span>
</div>
<div class="is-size-5 publication-university">
<span class="author-block">The University of Texas at Arlington</span>
</div>
<div class="is-size-5 publication-lab">
<span class="author-block">
<a href="https://rvl.uta.edu">Robotic Vision Laboratory</a></span>
</div>
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<div class="publication-links">
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<span>Preprint</span>
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<span>Source Code</span>
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<span>Dataset</span>
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</section>
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</section>
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<div class="container is-max-desktop" id="Abstract">
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<h2 class="title is-3">Abstract</h2>
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<p>
In agriculture, automating the accurate tracking of fruits,
vegetables, and fiber is a very tough problem. The issue becomes
extremely challenging in dynamic field environments. Yet, this
information is critical for making day-to-day agricultural
decisions, assisting breeding programs, and much more. To tackle
this dilemma, we introduce <strong>NTrack</strong>, a novel
multiple object tracking framework based on the linear
relationship between the locations of <em>neighboring</em>
tracks. <strong>NTrack</strong> computes dense optical flow and
utilizes particle filtering to guide each tracker.
Correspondences between detections and tracks are found through
data association via direct observations and indirect cues, which
are then combined to obtain an updated observation. Our modular
multiple object tracking system is independent of the underlying
detection method, thus allowing for the interchangeable use of
any off-the-shelf object detector. We show the efficacy of our
approach on the task of tracking and counting infield cotton
bolls. Experimental results show that our system exceeds
contemporary tracking and cotton boll-based counting methods by a
large margin. Furthermore, we publicly release the
<em>first</em> annotated cotton boll video dataset to the
research community.
</p>
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</div>
</div>
</div>
<br>
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<!-- Dataset -->
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<h2 class="title is-3">Dataset</h2>
<div class="content has-text-justified">
<p>
The <strong>TexCot22</strong> dataset is a set of cotton crop video
sequences for training and testing multi-object tracking methods.
Each tracking sequence is 10 to 20 seconds in length. The dataset
contains of a total of 30 sequences of which 17 are for training
and the remaining 13 are for testing. Among the training sequences,
2 of them consist of roughly 5,000 annotated images, which can be
used to train a cotton boll detection model. The video sequences
were captured at 4K resolution and at distinct frame rates (e.g.,
10, 15, 30). There are typically 2 to 10 cotton bolls per cluster.
The average width and height of an annotated bounding box is
approximately 230 x 210 pixels. To make the dataset robust to
environmental conditions, we recorded the field videos at separate
times of day to account for varying lighting conditions. In total,
there are roughly 30 x 300 frames with 150,000 labeled instances.
On average there are 70 unique cotton bolls in each sequence.
</p>
</div>
</div>
</div>
</div>
<br>
<div class="container is-max-desktop content" id="Citation">
<!-- Citation -->
<h2 class="title is-3">Citation</h2>
<p>
If you find this project useful, then please consider citing both our
paper and dataset.
</p>
<pre><code>@article{muzaddid2023ntrack,
title={NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting},
author={Muzaddid, Md Ahmed Al and Beksi, William J},
journal={IEEE Transactions on Automation Science and Engineering},
volume={},
number={},
pages={1--13},
doi={10.1109/TASE.2023.3342791},
year={2023}
}</code></pre>
<pre><code>@data{T8/5M9NCI_2024,
title={{TexCot22}},
author={Muzaddid, Md Ahmed Al and Beksi, William J},
publisher={Texas Data Repository},
version={V2},
url={https://doi.org/10.18738/T8/5M9NCI},
doi={10.18738/T8/5M9NCI},
year={2024}
}</code></pre>
</div>
<br>
<div class="container is-max-desktop" id="License">
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<h2 class="title is-3">License</h2>
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<p>
<strong>NTrack</strong> is licensed under the
<a href="https://www.apache.org/licenses/LICENSE-2.0">Apache License, Version 2.0</a>.
The <strong>TexCot22</strong> dataset is available for non-commercial use under the
<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License ("CC BY-NC-SA 4.0")</a>.
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