Event-based Action Recognition Using Timestamp Image Encoding Network

TL;DR

This paper introduces a timestamp image encoding network for event-based action recognition, effectively capturing spatial-temporal information from event camera data to match RGB-based benchmarks and achieve state-of-the-art gesture recognition results.

Contribution

The paper proposes a novel timestamp image encoding method combined with a 2D network for event-based action recognition, demonstrating competitive performance.

Findings

Achieves comparable performance to RGB-based methods on real-world action recognition.

Attains state-of-the-art results on gesture recognition.

Efficient encoding of event data for standard vision models.

Abstract

Event camera is an asynchronous, high frequency vision sensor with low power consumption, which is suitable for human action recognition task. It is vital to encode the spatial-temporal information of event data properly and use standard computer vision tool to learn from the data. In this work, we propose a timestamp image encoding 2D network, which takes the encoded spatial-temporal images of the event data as input and output the action label. Experiment results show that our method can achieve the same level of performance as those RGB-based benchmarks on real world action recognition, and also achieve the SOTA result on gesture recognition.