论文标题
bot-sort：强大的关联多培训跟踪
BoT-SORT: Robust Associations Multi-Pedestrian Tracking
论文作者
论文摘要
多对象跟踪（MOT）的目标是检测和跟踪场景中的所有对象，同时为每个对象保留一个唯一的标识符。在本文中，我们提出了一个新的可靠的最新跟踪器，可以将运动和外观信息的优势与摄像机补偿以及更准确的Kalman Filter状态向量相结合。我们的新跟踪器在Mot17和Mot20测试集的Motchallenge [29，11]的数据集[29，11]中，Bot-Sort-Sort和Bot-Sort-Reid排名第一，就所有主要MOT指标而言：Mota，IDF1和Hota。对于Mot17：80.5 Mota，80.2 IDF1和65.0 HOTA。源代码和预培训模型可在https://github.com/niraharon/bot-sort上找到
The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object. In this paper, we present a new robust state-of-the-art tracker, which can combine the advantages of motion and appearance information, along with camera-motion compensation, and a more accurate Kalman filter state vector. Our new trackers BoT-SORT, and BoT-SORT-ReID rank first in the datasets of MOTChallenge [29, 11] on both MOT17 and MOT20 test sets, in terms of all the main MOT metrics: MOTA, IDF1, and HOTA. For MOT17: 80.5 MOTA, 80.2 IDF1, and 65.0 HOTA are achieved. The source code and the pre-trained models are available at https://github.com/NirAharon/BOT-SORT
