  Optical flow computation with frame-based cameras provides high accuracy but
the speed is limited either by the model size of the algorithm or by the frame
rate of the camera. This makes it inadequate for high-speed applications. Event
cameras provide continuous asynchronous event streams overcoming the frame-rate
limitation. However, the algorithms for processing the data either borrow frame
like setup limiting the speed or suffer from lower accuracy. We fuse the
complementary accuracy and speed advantages of the frame and event-based
pipelines to provide high-speed optical flow while maintaining a low error
rate. Our bio-mimetic network is validated with the MVSEC dataset showing 19%
error degradation at 4x speed up. We then demonstrate the system with a
high-speed drone flight scenario where a high-speed event camera computes the
flow even before the optical camera sees the drone making it suited for
applications like tracking and segmentation. This work shows the fundamental
trade-offs in frame-based processing may be overcome by fusing data from other
modalities.

Computer Vision and Pattern Recognition;Hardware Architecture;Neural and Evolutionary Computing;Image and Video Processing