MONA: Moving Object Detection from Videos Shot by Dynamic Camera
This addresses moving object detection for urban planning applications, but appears incremental as it builds on existing methods like LEAP-VO and Segment Anything.
The paper tackles the challenge of distinguishing camera-induced from object motion in dynamic urban environments by introducing MONA, a framework for moving object detection and segmentation from videos shot by dynamic cameras, achieving state-of-the-art results on the MPI Sintel dataset.
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.