动态相机视频中精准检测运动物体，解决相机晃动干扰问题。
MONA: Moving Object Detection from Videos Shot by Dynamic Camera
- 通过光流与点跟踪提取动态点，区分相机运动与物体运动
- 在MPI Sintel数据集上达到当前最佳效果，显著提升分割精度
- 适合城市规划、自动驾驶等需要稳定目标检测的场景
动态城市环境中，相机和物体均在移动，给相机轨迹估计带来挑战，因为难以区分相机运动与物体运动。我们提出MONA，一种针对动态相机拍摄视频的鲁棒运动物体检测与分割新框架。MONA包含两个核心模块：动态点提取模块，利用光流与任意点跟踪识别动态点；运动物体分割模块，结合自适应边界框过滤与Segment Anything实现精确分割。我们将MONA与相机轨迹估计方法LEAP-VO集成，在MPI Sintel数据集上取得当前最优性能，验证了其在运动物体检测上的有效性，并展示了其在城市规划等领域的广泛应用潜力。
原文摘要 · Abstract (English)
Dynamic urban environments, characterized by moving cameras and objects, pose significant challenges for camera trajectory estimation by complicating the distinction between camera-induced and object motion. We introduce MONA, a novel framework designed for robust moving object detection and segmentation from videos shot by dynamic cameras. MONA comprises two key modules: Dynamic Points Extraction, which leverages optical flow and tracking any point to identify dynamic points, and Moving Object Segmentation, which employs adaptive bounding box filtering, and the Segment Anything for precise moving object segmentation. We validate MONA by integrating with the camera trajectory estimation method LEAP-VO, and it achieves state-of-the-art results on the MPI Sintel dataset comparing to existing methods. These results demonstrate MONA's effectiveness for moving object detection and its potential in many other applications in the urban planning field.
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