Abstract
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.
Abstract (translated)
从移动摄像头视角分离运动物体和静态物体对于机器人技术中的3D重建、自主导航和场景理解至关重要。现有方法主要依赖于光流，这在涉及相机移动的复杂结构化场景中难以检测到移动对象。为了解决这一局限性，我们提出了聚焦扩展可能性与分割（FoELS）的方法，该方法基于将光流信息与纹理信息整合的核心理念。FoELS通过光流计算出焦点扩展点（FoE），并从FoE计算的异常值推导出初步运动概率。随后，这种方法会将其与基于分割的先验知识融合以估计最终的移动可能性。FoELS能够有效处理复杂结构场景、相机旋转和平行移动等挑战。在DAVIS 2016数据集和真实世界交通视频上的全面评估表明了该方法的有效性和最先进的性能。
URL
https://arxiv.org/abs/2507.13628
