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Event cameras provide rich signals that are suitable for motion estimationsince they respond to changes in the scene. As any visual changes in the sceneproduce event data, it is paramount to classify the data into different motions(i.e., motion segmentation), which is useful for various tasks such as objectdetection and visual servoing. We propose an iterative motion segmentationmethod, by classifying events into background (e.g., dominant motionhypothesis) and foreground (independent motion residuals), thus extending theContrast Maximization framework. Experimental results demonstrate that theproposed method successfully classifies event clusters both for public andself-recorded datasets, producing sharp, motion-compensated edge-like images.The proposed method achieves state-of-the-art accuracy on moving objectdetection benchmarks with an improvement of over 30%, and demonstrates itspossibility of applying to more complex and noisy real-world scenes. We hopethis work broadens the sensitivity of Contrast Maximization with respect toboth motion parameters and input events, thus contributing to theoreticaladvancements in event-based motion segmentation estimation.https://github.com/aoki-media-lab/event_based_segmentation_vcmax

Iterative Event-based Motion Segmentation by Variational Contrast Maximization