NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting
This addresses the need for automated agricultural monitoring to support decision-making and breeding programs, representing a domain-specific incremental advancement.
The authors tackled the problem of accurately tracking and counting cotton bolls in dynamic field environments by introducing NTrack, a multiple object tracking framework, which exceeded contemporary methods by a large margin and included the release of the first annotated cotton boll video dataset.
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.