Recursive Contrast Maximization for Event-Based High-Frequency Motion Estimation

Achieving high-frequency motion estimation with a fast-moving camera is an important task in the field of computer vision. Contrast Maximization (CMax), a method of motion estimation using an event camera, is the de-facto standard. However, CMax requires the processing of a large number of events at a single time, a computationally expensive task. That makes it difficult to perform high-frequency estimates. Specifically, past events that have already been used once for estimation need to be evaluated again. In this paper, we propose 'Recursive Contrast Maximization (R-CMax)' to estimate motions at high frequencies. The proposed method approximates multiple events by two 'compressed events' using estimated trajectories of events from the previous time step, which can be updated recursively. By using a small number of 'compressed events,' motion estimation can be updated efficiently. Comparing R-CMax with CMax and its extensions, we experimentally show that R-CMax can perform motion estimation with a fraction of the computational complexity while maintaining comparable accuracy.