CropTrack: 精密農業のための再識別追跡フレームワーク
CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture
農業環境での物体追跡において、見た目と動きの情報を組み合わせて個体の同一性を維持する新しい追跡フレームワークを提案し、従来手法より高い追跡精度を実現した。
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著者: Md Ahmed Al Muzaddid, Jordan A. James, William J. Beksi
分類: cs.CV, cs.RO
原文アブストラクト
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.
