基于光流与纹理融合，提升移动相机下动态物体检测精度
Moving Object Detection from Moving Camera Using Focus of Expansion Likelihood and Segmentation
- 结合光流与纹理信息，利用扩张中心估计运动概率
- 在DAVIS 2016和真实交通视频上达到领先性能
- 适合复杂结构场景和旋转运动下的机器人导航
从移动相机视角分离动态与静态物体对三维重建、自主导航和场景理解至关重要。现有方法多依赖光流，在包含相机运动的复杂结构场景中表现不佳。为此，我们提出聚焦扩张似然与分割（FoELS）方法，核心思想是融合光流与纹理信息。该方法通过光流计算聚焦扩张（FoE），从FoE计算的异常值中提取初始运动似然，并与基于分割的先验融合，得到最终动态概率。该方法有效应对复杂结构场景、相机旋转及平行运动等挑战。在DAVIS 2016数据集和真实交通视频上的全面评估表明其有效性与最先进性能。
原文摘要 · Abstract (English)
Separating moving and static objects from a moving camera viewpoint is essential for 3D reconstruction, autonomous navigation, and scene understanding in robotics. Existing approaches often rely primarily on optical flow, which struggles to detect moving objects in complex, structured scenes involving camera motion. To address this limitation, we propose Focus of Expansion Likelihood and Segmentation (FoELS), a method based on the core idea of integrating both optical flow and texture information. FoELS computes the focus of expansion (FoE) from optical flow and derives an initial motion likelihood from the outliers of the FoE computation. This likelihood is then fused with a segmentation-based prior to estimate the final moving probability. The method effectively handles challenges including complex structured scenes, rotational camera motion, and parallel motion. Comprehensive evaluations on the DAVIS 2016 dataset and real-world traffic videos demonstrate its effectiveness and state-of-the-art performance.
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