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

Fusing Frame and Event Vision for High-speed Optical Flow for Edge Application