AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture
Leonardo Saraceni, Ionut M. Motoi, Daniele Nardi, Thomas A. Ciarfuglia

TL;DR
AgriSORT is a lightweight, real-time tracking-by-detection framework designed for precision agriculture robotics, emphasizing motion-based association to handle challenging conditions like occlusions and static objects.
Contribution
It introduces AgriSORT, a simple and efficient online tracking method that relies solely on motion information, tailored for agricultural environments with high object density and similarity.
Findings
Achieves accurate tracking in challenging vineyard videos
Operates in real-time with minimal dependencies
Outperforms appearance-based trackers in static object scenarios
Abstract
The problem of multi-object tracking (MOT) consists in detecting and tracking all the objects in a video sequence while keeping a unique identifier for each object. It is a challenging and fundamental problem for robotics. In precision agriculture the challenge of achieving a satisfactory solution is amplified by extreme camera motion, sudden illumination changes, and strong occlusions. Most modern trackers rely on the appearance of objects rather than motion for association, which can be ineffective when most targets are static objects with the same appearance, as in the agricultural case. To this end, on the trail of SORT [5], we propose AgriSORT, a simple, online, real-time tracking-by-detection pipeline for precision agriculture based only on motion information that allows for accurate and fast propagation of tracks between frames. The main focuses of AgriSORT are efficiency,…
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Taxonomy
TopicsSmart Agriculture and AI · Food Supply Chain Traceability · Microbial infections and disease research
