论文标题
通过高斯工艺过滤在周期性运动下对物体的强大检测
Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering
论文作者
论文摘要
对象检测（OD）是计算机视觉中具有许多实际应用的重要任务。对于某些用例，必须在视频上完成OD，其中感兴趣的对象具有定期运动。在本文中，我们正式化了周期性OD的问题，该问题包括在特定情况下提高OD模型的性能，在这种情况下，感兴趣的对象正在重复相对于视频帧重复相似的时空轨迹。所提出的方法基于训练高斯过程以建模周期性运动，并使用它来过滤OD模型的错误预测。通过模拟各种OD模型和周期性轨迹，我们证明了完全数据驱动的过滤方法可以通过较大的边距提高检测性能。
Object Detection (OD) is an important task in Computer Vision with many practical applications. For some use cases, OD must be done on videos, where the object of interest has a periodic motion. In this paper, we formalize the problem of periodic OD, which consists in improving the performance of an OD model in the specific case where the object of interest is repeating similar spatio-temporal trajectories with respect to the video frames. The proposed approach is based on training a Gaussian Process to model the periodic motion, and use it to filter out the erroneous predictions of the OD model. By simulating various OD models and periodic trajectories, we demonstrate that this filtering approach, which is entirely data-driven, improves the detection performance by a large margin.
