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This paper presents a Dynamic Vision Sensor (DVS) based system for reasoningabout high speed motion. As a representative scenario, we consider the case ofa robot at rest reacting to a small, fast approaching object at speeds higherthan 15m/s. Since conventional image sensors at typical frame rates observesuch an object for only a few frames, estimating the underlying motion presentsa considerable challenge for standard computer vision systems and algorithms.In this paper we present a method motivated by how animals such as insectssolve this problem with their relatively simple vision systems.  Our solution takes the event stream from a DVS and first encodes the temporalevents with a set of causal exponential filters across multiple time scales. Wecouple these filters with a Convolutional Neural Network (CNN) to efficientlyextract relevant spatiotemporal features. The combined network learns to outputboth the expected time to collision of the object, as well as the predictedcollision point on a discretized polar grid. These critical estimates arecomputed with minimal delay by the network in order to react appropriately tothe incoming object. We highlight the results of our system to a toy dartmoving at 23.4m/s with a 24.73{\deg} error in ${\theta}$, 18.4mm averagediscretized radius prediction error, and 25.03% median time to collisionprediction error.

Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors