Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation

TL;DR

This paper introduces a timestamp image encoding network for human action recognition and anticipation using event camera data, achieving competitive results and state-of-the-art performance in gesture recognition.

Contribution

It proposes a novel timestamp image encoding method and a future timestamp image generator to improve action recognition and anticipation from event data.

Findings

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

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

Future timestamp image generation enhances prediction accuracy for incomplete actions.

Abstract

Event camera is an asynchronous, high frequency vision sensor with low power consumption, which is suitable for human action understanding 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 with polarity information of the event data as input and output the action label. In addition, we propose a future timestamp image generator to generate futureaction information to aid the model to anticipate the human action when the action is not completed. 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 state of the art (SOTA) result on gesture recognition. Our future timestamp image generating model can effectively improve the prediction accuracy when the action is not completed. We also provide insight discussion on the importance of motion and appearance information in action recognition and anticipation.