Event-based Motion Segmentation with Spatio-Temporal Graph Cuts (2012.08730v3)

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.

• The paper proposes casting event-based motion segmentation as an energy minimization problem solved using iterative spatio-temporal graph cuts on event data.
• This method effectively segments multiple independently moving objects while handling camera ego-motion, identifying them without needing to know their count.
• Experiments demonstrate state-of-the-art performance on multiple datasets, showing practical potential for real-time scene understanding in robotics and autonomous systems.

Event-Based Motion Segmentation with Spatio-Temporal Graph Cuts

The paper "Event-based Motion Segmentation with Spatio-Temporal Graph Cuts" presents a novel approach to motion segmentation using event-based cameras. Event-based cameras differ from traditional cameras by capturing asynchronous pixel-wise intensity changes, known as "events," which allow for high temporal resolution and dynamic range.

Problem Statement

Event-based motion segmentation aims to classify the asynchronous events generated by an event camera into groups representing coherent moving objects. This is particularly challenging when the camera itself is moving, as the events can be generated both by independently moving objects (IMOs) and by the scene motion due to the camera's ego-motion.

Proposed Methodology

The authors propose casting the event-based motion segmentation problem as an energy minimization task. This involves fitting multiple motion models to the event data and jointly solving two subproblems:

1. Event-cluster assignment (labeling): Determining which events belong to which independent motion.
2. Motion model fitting: Estimating the parameters of the motion models that best represent the events within each assigned cluster.

The methodology leverages the unique structure of event data, represented as a spatio-temporal graph. The paper introduces a hierarchical subdivision strategy to efficiently initialize a pool of motion model candidates. These models are then dynamically refined through an iterative graph-cut approach, aiming for globally consistent segmentation results that are spatially coherent and require the fewest number of clusters.

Evaluation and Results

Experiments on available datasets demonstrate the method's versatility across various scenes featuring different motion patterns and varying numbers of moving objects. The results yielded state-of-the-art performance without the need to predetermine the number of IMOs. Quantitative evaluations using detection rate and IoU metrics across different datasets (EED, EVIMO, EMSMC) showcase the effectiveness of the approach.

Implications and Future Directions

Practically, this research demonstrates the potential for real-time scene understanding in robotics and autonomous systems using event cameras. Theoretical implications include the adaptive modeling of spatio-temporal data using graph-based methods. Future directions may explore the integration of this approach with additional sensor modalities or the extension to more complex dynamic scenes. Given the nature of event cameras, innovations in hardware and software could further enhance the efficiency and applicability of this segmentation approach.

In conclusion, "Event-based Motion Segmentation with Spatio-Temporal Graph Cuts" adds significant value to the field by introducing a robust framework for consistently segmenting motion in highly dynamic scenarios, exploiting the unique advantages of event-based sensors. The open-source release of software and datasets by the authors ensures that this research can be a foundational building block for future explorations in event-based vision.