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 identification F1 and association accuracy scores with a lower number of identity switches.
Abstract (translated)
在农业环境中进行多目标跟踪（MOT）面临着诸多挑战，包括重复模式、相似物体外观、突然的光照变化以及频繁的遮挡。当前领域的追踪器依赖于对象的运动而非外观来进行关联，但它们难以维持目标在经历频繁且强烈的遮挡时的身份一致性。由于物体外观的高度相似性，基于外观的关联在农业场景中变得复杂。 为了解决这个问题，我们提出了一种新的MOT框架——CropTrack，该框架结合了外观和运动信息。CropTrack集成了增强重排序的外观关联、一对一多对多的外观冲突解决策略以及指数移动平均原型特征库来改进基于外观的关联。在公开的农业MOT数据集上进行评估时，CropTrack展示了持续的身份保持能力，并且优于传统的仅基于运动的方法。与最先进的方法相比，CropTrack在识别F1和关联准确度得分方面实现了显著提升，并且身份切换的数量较少。
URL
https://arxiv.org/abs/2512.24838
