MONA: Moving Object Detection from Videos Shot by Dynamic Camera

TL;DR

MONA is a new framework that improves moving object detection and segmentation in videos captured by dynamic cameras, addressing challenges posed by camera and object motion in urban environments.

Contribution

MONA introduces a novel two-module approach combining dynamic points extraction and adaptive segmentation, advancing moving object detection in videos with moving cameras.

Findings

Achieves state-of-the-art results on MPI Sintel dataset.

Effectively distinguishes camera motion from object motion.

Enhances urban environment analysis applications.

Abstract

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.