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.

通过自动化追踪技术，基于线性关系以及通过数据关联找到检测与追踪之间的对应关系，利用粒子滤波和稠密光流来引导追踪器，该论文介绍了一种独立于检测方法的模块化多物体追踪系统 NTrack，并通过实验证明其在棉铃追踪和计数方面的有效性，超过了其他方法，并公开了第一个棉铃视频数据集。