Hypergraph-Based Asynchronous Event Processing for Moving Object Classification

Unlike traditional video cameras, event cameras capture asynchronous event streams in which each event encodes pixel location, triggers’ timestamps, and the polarity of brightness changes. In this paper, we introduce a novel hypergraph-based framework for moving object classification. Specifically, we capture moving objects with an event camera, to perceive and collect asynchronous event streams in a high temporal resolution. Unlike stacked event frames, we encode asynchronous event data into a hypergraph, fully mining the high-order correlation of event data, and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition. The experimental results show that our method has a good performance in moving object classification (e.g., gait identification).