论文标题
可变性：具有可变速度对象运动的多对象跟踪
VariabilityTrack:Multi-Object Tracking with Variable Speed Object Movement
论文作者
论文摘要
多对象跟踪（MOT）旨在估计视频中对象的边界框和身份。大多数方法可以大致分类为逐项跟踪和联合检测 - 缔合范例。尽管后者引起了更多的关注并表现出了比前者的相对性相对相对的相对性能，但我们声称，在跟踪准确性方面，逐个检测范式仍然是最佳解决方案，例如Bytetrack，它可以在30.3 IDF1和63.1 HOTA上与30 fps pers of SpearS ocy op Speare perseptive ocy of Spect vers of Single vers persective of Speare persective of Spect and vef and and the and the and the Mota，77.3 Mota，77.3 IDF1和63.1 Hota persection。无人机加速，使用统一卡尔曼过滤器的这种跟踪器的性能将受到很大的影响，从而导致跟踪损失。在本文中，我们提出了基于环境反馈并改善匹配过程的可变速度卡尔曼滤波器算法，这可以极大地改善可变速度的速度效果，同时保持高度跟踪的速度，同时维持高度稳定的场景。最终，在MOT17测试集上可以实现更高的MOTA和IDF1结果
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods can be roughly classified as tracking-by-detection and joint-detection-association paradigms. Although the latter has elicited more attention and demonstrates comparable performance relative than the former, we claim that the tracking-by-detection paradigm is still the optimal solution in terms of tracking accuracy,such as ByteTrack,which achieves 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU.However, under complex perspectives such as vehicle and UAV acceleration, the performance of such a tracker using uniform Kalman filter will be greatly affected, resulting in tracking loss.In this paper, we propose a variable speed Kalman filter algorithm based on environmental feedback and improve the matching process, which can greatly improve the tracking effect in complex variable speed scenes while maintaining high tracking accuracy in relatively static scenes. Eventually, higher MOTA and IDF1 results can be achieved on MOT17 test set than ByteTrack
