基于变分对比最大化，迭代分离事件相机中的运动与背景。
Iterative Event-based Motion Segmentation by Variational Contrast Maximization
- 通过变分对比最大化，迭代区分背景运动与前景独立运动残差。
- 在公开与自录数据集上实现30%以上精度提升，边缘图像清晰锐利。
- 适合高动态、复杂噪声场景下的运动分割，推动事件视觉理论发展。
事件相机对场景变化敏感，能生成丰富信号用于运动估计。由于任何视觉变化都会触发事件数据，因此将事件数据分类为不同运动（即运动分割）至关重要，有助于目标检测和视觉伺服等任务。本文提出一种迭代运动分割方法，通过将事件分为背景（如主导运动假设）和前景（独立运动残差），扩展了对比最大化框架。实验表明，该方法在公开及自录数据集上均成功分类事件簇，生成锐利、运动补偿的边缘图像。在移动物体检测基准测试中达到领先精度，性能提升超过30%，并验证其在复杂噪声真实场景中的适用性。我们希望该工作提升对比最大化对运动参数和输入事件的敏感度，推动事件基运动分割估计的理论进展。
原文摘要 · Abstract (English)
Event cameras provide rich signals that are suitable for motion estimation since they respond to changes in the scene. As any visual changes in the scene produce event data, it is paramount to classify the data into different motions (i.e., motion segmentation), which is useful for various tasks such as object detection and visual servoing. We propose an iterative motion segmentation method, by classifying events into background (e.g., dominant motion hypothesis) and foreground (independent motion residuals), thus extending the Contrast Maximization framework. Experimental results demonstrate that the proposed method successfully classifies event clusters both for public and self-recorded datasets, producing sharp, motion-compensated edge-like images. The proposed method achieves state-of-the-art accuracy on moving object detection benchmarks with an improvement of over 30%, and demonstrates its possibility of applying to more complex and noisy real-world scenes. We hope this work broadens the sensitivity of Contrast Maximization with respect to both motion parameters and input events, thus contributing to theoretical advancements in event-based motion segmentation estimation. https://github.com/aoki-media-lab/event_based_segmentation_vcmax
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