Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting

TL;DR

This paper introduces a cascaded two-level multi-model fitting approach for motion segmentation using event-based cameras, effectively identifying independent moving objects in challenging dynamic scenes.

Contribution

It presents a novel two-level method combining feature tracking, multi-model fitting, and graph-cut clustering for accurate motion segmentation with monocular event cameras.

Findings

Effective in real-world scenes with various motion patterns.

Handles an unknown number of moving objects.

Outperforms existing methods in accuracy and efficiency.

Abstract

Among prerequisites for a synthetic agent to interact with dynamic scenes, the ability to identify independently moving objects is specifically important. From an application perspective, nevertheless, standard cameras may deteriorate remarkably under aggressive motion and challenging illumination conditions. In contrast, event-based cameras, as a category of novel biologically inspired sensors, deliver advantages to deal with these challenges. Its rapid response and asynchronous nature enables it to capture visual stimuli at exactly the same rate of the scene dynamics. In this paper, we present a cascaded two-level multi-model fitting method for identifying independently moving objects (i.e., the motion segmentation problem) with a monocular event camera. The first level leverages tracking of event features and solves the feature clustering problem under a progressive multi-model fitting scheme. Initialized with the resulting motion model instances, the second level further addresses the event clustering problem using a spatio-temporal graph-cut method. This combination leads to efficient and accurate event-wise motion segmentation that cannot be achieved by any of them alone. Experiments demonstrate the effectiveness and versatility of our method in real-world scenes with different motion patterns and an unknown number of independently moving objects.