The problem of multi-object tracking (MOT) consists in detecting and tracking all the objects in a video sequence while keeping a unique identifier for each object. It is a challenging and fundamental problem for robotics. In precision agriculture the challenge of achieving a satisfactory solution is amplified by extreme camera motion, sudden illumination changes, and strong occlusions. Most modern trackers rely on the appearance of objects rather than motion for association, which can be ineffective when most targets are static objects with the same appearance, as in the agricultural case. To this end, on the trail of SORT [5], we propose AgriSORT, a simple, online, real-time tracking-by-detection pipeline for precision agriculture based only on motion information that allows for accurate and fast propagation of tracks between frames. The main focuses of AgriSORT are efficiency, flexibility, minimal dependencies, and ease of deployment on robotic platforms. We test the proposed pipeline on a novel MOT benchmark specifically tailored for the agricultural context, based on video sequences taken in a table grape vineyard, particularly challenging due to strong self-similarity and density of the instances. Both the code and the dataset are available for future comparisons.

翻译：多目标跟踪（MOT）问题涉及检测并跟踪视频序列中的所有对象，同时为每个对象保持唯一标识符。这是机器人领域一项具有挑战性的基础问题。在精准农业中，极端的相机运动、突发的光照变化以及强烈的遮挡进一步加剧了实现满意解决方案的难度。大多数现代跟踪器依赖物体的外观而非运动进行关联，但在农业场景中，当多数目标为外观相同的静态物体时，这种方法可能效果不佳。为此，受SORT[5]启发，我们提出AgriSORT——一种专为精准农业设计的简单、在线、实时的基于检测的跟踪流水线，仅依赖运动信息实现帧间轨迹的精确快速传播。AgriSORT的核心在于高效性、灵活性、最小依赖关系以及便于在机器人平台上部署。我们基于从鲜食葡萄园采集的视频序列，在专门针对农业场景构建的新型MOT基准上对提出的流水线进行测试，该场景因实例的高度自相似性和密集性而极具挑战性。代码与数据集均已公开，可供后续比较研究。