Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization
This work addresses feature tracking for event cameras, which is incremental as it builds on prior methods with a sliding-window optimization approach.
The authors tackled the problem of continuous-time feature tracking in event cameras by aligning events along an estimated trajectory to maximize sharpness in event patch images, resulting in longer and more accurate feature tracks than previous methods on a public dataset.
We propose a novel method for continuous-time feature tracking in event cameras. To this end, we track features by aligning events along an estimated trajectory in space-time such that the projection on the image plane results in maximally sharp event patch images. The trajectory is parameterized by $n^{th}$ order B-splines, which are continuous up to $(n-2)^{th}$ derivative. In contrast to previous work, we optimize the curve parameters in a sliding window fashion. On a public dataset we experimentally confirm that the proposed sliding-window B-spline optimization leads to longer and more accurate feature tracks than in previous work.