Event-based Motion Segmentation with Spatio-Temporal Graph Cuts

TL;DR

This paper introduces a novel method for motion segmentation using event-based cameras, leveraging spatio-temporal graph cuts to accurately identify moving objects without prior knowledge of their number.

Contribution

It presents a new energy minimization framework that jointly solves event clustering and motion model fitting, advancing event-based motion segmentation techniques.

Findings

Achieves state-of-the-art results on available datasets.

Handles scenes with varying motion patterns and number of objects.

Does not require predefining the number of moving objects.

Abstract

Identifying independently moving objects is an essential task for dynamic scene understanding. However, traditional cameras used in dynamic scenes may suffer from motion blur or exposure artifacts due to their sampling principle. By contrast, event-based cameras are novel bio-inspired sensors that offer advantages to overcome such limitations. They report pixelwise intensity changes asynchronously, which enables them to acquire visual information at exactly the same rate as the scene dynamics. We develop a method to identify independently moving objects acquired with an event-based camera, i.e., to solve the event-based motion segmentation problem. We cast the problem as an energy minimization one involving the fitting of multiple motion models. We jointly solve two subproblems, namely event cluster assignment (labeling) and motion model fitting, in an iterative manner by exploiting the structure of the input event data in the form of a spatio-temporal graph. Experiments on available datasets demonstrate the versatility of the method in scenes with different motion patterns and number of moving objects. The evaluation shows state-of-the-art results without having to predetermine the number of expected moving objects. We release the software and dataset under an open source licence to foster research in the emerging topic of event-based motion segmentation.