Space-Time Event Clouds for Gesture Recognition: From RGB Cameras to Event Cameras

Abstract

The recently developed event cameras can directly sense the motion in the scene by generating an asynchronous sequence of events, i.e., event streams, where each individual event (x, y, t) corresponds to the space-time location when a pixel sensor captures an intensity change. Compared with RGB cameras, event cameras are frameless but can capture much faster motion, therefore have great potential for recognizing gestures of fast motions. To deal with the unique output of event cameras, previous methods often treat event streams as time sequences, thus do not fully explore the space-time sparsity of the event stream data. In this work, we treat the event stream as a set of 3D points in space-time, i.e., space-time event clouds. To analyze event clouds and recognize gestures, we propose to leverage PointNet, a neural network architecture originally designed for matching and recognizing 3D point clouds. We further adapt PointNet to cater to event clouds for real-time gesture recognition. On the benchmark dataset of event camera based gesture recognition, i.e., IBM DVS128 Gesture dataset, our proposed method achieves a high accuracy of 97.08% and performs the best among existing methods.