Moving Object Detection from Moving Camera Using Focus of Expansion Likelihood and Segmentation

TL;DR

This paper introduces FoELS, a novel method combining optical flow and texture segmentation to improve moving object detection from a moving camera, outperforming existing approaches in complex scenes.

Contribution

FoELS integrates focus of expansion likelihood with segmentation to robustly detect moving objects under various challenging camera motions.

Findings

Achieves state-of-the-art results on DAVIS 2016 dataset.

Effectively handles complex structured scenes and rotational camera motion.

Demonstrates robustness in real-world traffic videos.

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.