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.

翻译：多目标跟踪在农业环境中面临重大挑战，包括重复模式、相似外观、突然光照变化和频繁遮挡。当前该领域的跟踪器依赖物体运动而非外观进行关联，但在目标经历频繁且强烈遮挡时难以维持身份一致性。物体外观的高度相似性使得在农业场景中集成基于外观的关联具有挑战性。为解决该问题，本文提出CropTrack——一种基于外观与运动信息融合的新型多目标跟踪框架。CropTrack集成了重排序增强的外观关联、基于外观冲突解决策略的一对多关联，以及指数移动平均原型特征库，以提升基于外观的关联性能。在公开农业多目标跟踪数据集上的评估表明，CropTrack实现了稳定的身份保持能力，优于传统基于运动的跟踪方法。与现有最优方法相比，CropTrack在关联准确率和身份识别精度指标上取得显著提升，同时降低了身份切换次数。