Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor
This work addresses optical flow estimation for event-based vision systems, which is incremental as it builds on existing DVS methods to improve accuracy by integrating segmentation.
The authors tackled the problem of estimating optical flow from dynamic vision sensor (DVS) events, which are asynchronous and high-temporal-resolution, by developing the SOFAS algorithm that simultaneously segments objects moving at the same velocity to avoid the aperture problem, resulting in more accurate flow estimates compared to traditional methods.
We present an algorithm (SOFAS) to estimate the optical flow of events generated by a dynamic vision sensor (DVS). Where traditional cameras produce frames at a fixed rate, DVSs produce asynchronous events in response to intensity changes with a high temporal resolution. Our algorithm uses the fact that events are generated by edges in the scene to not only estimate the optical flow but also to simultaneously segment the image into objects which are travelling at the same velocity. This way it is able to avoid the aperture problem which affects other implementations such as Lucas-Kanade. Finally, we show that SOFAS produces more accurate results than traditional optic flow algorithms.