  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, flexibility, minimal
dependencies, and ease of deployment on robotic platforms. We test the proposed
pipeline on a novel MOT benchmark specifically tailored for the agricultural
context, based on video sequences taken in a table grape vineyard, particularly
challenging due to strong self-similarity and density of the instances. Both
the code and the dataset are available for future comparisons.
