# NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting

arXiv: https://arxiv.org/abs/2312.10922 · HTML: https://mlsanity.com/paper/2312.10922/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [POI: Multiple Object Tracking with High Performance Detection and Appearance Feature](https://mlsanity.com/paper/1610.06136/) (2016)
- [Deep Cosine Metric Learning for Person Re-Identification](https://mlsanity.com/paper/1812.00442/) (2018)
- [Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor](https://mlsanity.com/paper/1504.02340/) (2015)
- [Apple Counting using Convolutional Neural Networks](https://mlsanity.com/paper/2208.11566/) (2022)

## Built on by (most cited first, 2 of 2)

- [CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture](https://mlsanity.com/paper/2512.24838/) (2025)
- [CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting](https://mlsanity.com/paper/2601.00207/) (2026)

## Abstract (arXiv)

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 NTrack, a novel multiple object tracking framework based on the linear relationship between the locations of neighboring tracks. NTrack 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 first annotated cotton boll video dataset to the research community.