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CropTrack addresses a critical gap in agricultural computer vision by solving identity preservation issues caused by occlusions and visual similarity. The framework successfully integrates appearance-based association with motion cues, offering measurable improvements over traditional methods that rely solely on movement data. This advancement is significant for deploying robust autonomous systems in complex farm environments where object tracking reliability directly impacts operational efficiency.
CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture
arXiv:2512.24838v2 Announce Type: replace-cross Abstract: Multiple-object tracking (MOT) in agricultural environments presents major challenges due to repetitive patterns, similar object appearances, sudden illumination changes, and frequent occlusions. Contemporary trackers in this domain rely on the motion of objects rather than appearance for association. Nevertheless, they struggle to maintain object identities when targets undergo frequent and strong occlusions. The high similarity of object appearances makes integrating appearance-based association nontrivial for agricultural scenarios. To solve this problem we propose CropTrack, a novel MOT framework based on the combination of appearance and motion information. CropTrack integrates a reranking-enhanced appearance association, a one-to-many association with appearance-based conflict resolution strategy, and an exponential moving average prototype feature bank to improve appearance-based association. Evaluated on publicly available agricultural MOT datasets, CropTrack demonstrates consistent identity preservation, outperforming traditional motion-based tracking methods. Compared to the state of the art, CropTrack achieves significant gains in association accuracy and identification precision scores with a lower number of identity switches.
Abstract:Multiple-object tracking (MOT) in agricultural environments presents major challenges due to repetitive patterns, similar object appearances, sudden illumination changes, and frequent occlusions. Contemporary trackers in this domain rely on the motion of objects rather than appearance for association. Nevertheless, they struggle to maintain object identities when targets undergo frequent and strong occlusions. The high similarity of object appearances makes integrating appearance-based association nontrivial for agricultural scenarios. To solve this problem we propose CropTrack, a novel MOT framework based on the combination of appearance and motion information. CropTrack integrates a reranking-enhanced appearance association, a one-to-many association with appearance-based conflict resolution strategy, and an exponential moving average prototype feature bank to improve appearance-based association. Evaluated on publicly available agricultural MOT datasets, CropTrack demonstrates consistent identity preservation, outperforming traditional motion-based tracking methods. Compared to the state of the art, CropTrack achieves significant gains in association accuracy and identification precision scores with a lower number of identity switches.
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
arXiv:2512.24838 [cs.CV]
(or arXiv:2512.24838v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2512.24838 Focus to learn more arXiv-issued DOI via DataCite
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