论文标题
搜索track：具有对象定位的搜索和运动感知功能的多个对象跟踪
SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features
论文作者
论文摘要
该论文提出了一种新方法，搜索导流，用于多个对象跟踪和分割（MOTS）。为了解决检测到的对象之间的关联问题，搜索track提出了对象定制的搜索和运动感知功能。通过维护每个对象的Kalman滤波器，我们将预测的运动编码为运动吸引功能，其中包括运动和外观提示。对于每个对象，通过学习针对特定对象的动态卷积的一组权重来创建自定义的完全卷积搜索引擎。实验表明，我们的搜索方法在MOT和MOT任务上都优于竞争方法，尤其是在关联精度方面。我们的方法在Kitti Mots上实现了71.5 HOTA（CAR）和57.6 HOTA（行人），而Mot17上的53.4 HOTA。就关联精度而言，我们的方法在Kitti MOTS上的2D在线方法中实现了最先进的性能。我们的代码可从https://github.com/qa276390/searchtrack获得。
The paper presents a new method, SearchTrack, for multiple object tracking and segmentation (MOTS). To address the association problem between detected objects, SearchTrack proposes object-customized search and motion-aware features. By maintaining a Kalman filter for each object, we encode the predicted motion into the motion-aware feature, which includes both motion and appearance cues. For each object, a customized fully convolutional search engine is created by SearchTrack by learning a set of weights for dynamic convolutions specific to the object. Experiments demonstrate that our SearchTrack method outperforms competitive methods on both MOTS and MOT tasks, particularly in terms of association accuracy. Our method achieves 71.5 HOTA (car) and 57.6 HOTA (pedestrian) on the KITTI MOTS and 53.4 HOTA on MOT17. In terms of association accuracy, our method achieves state-of-the-art performance among 2D online methods on the KITTI MOTS. Our code is available at https://github.com/qa276390/SearchTrack.
