CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture
This addresses tracking challenges for precision agriculture, offering improved identity preservation in scenarios with high object similarity and occlusions, though it appears incremental as it builds on existing MOT frameworks.
The paper tackled the problem of multiple-object tracking in agricultural environments, where repetitive patterns and occlusions challenge identity preservation, by proposing CropTrack, a framework combining appearance and motion information, which outperformed traditional methods with significant gains in identification F1 and association accuracy scores and fewer identity switches.
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 identification F1 and association accuracy scores with a lower number of identity switches.