An Asynchronous LLM Architecture for Event Stream Analysis with Cameras

Authors

  • Zeyu Wang University of California, Los Angeles, USA
  • Zong Cheng Chu ByteDance, USA
  • Minghao Chen foshan top2top Technology Co. Ltd., China
  • Yiqian Zhang State Key Laboratory of Biotherapy, West China Hospital, China
  • Rui Yang University of California, Riverside, USA

DOI:

https://doi.org/10.5281/zenodo.13639724

Keywords:

llm architecture, stream analysis, pixel, cameras

Abstract

Event-based cameras, as bio-inspired vision sensors, record intensity changes asynchronously. The Dynamic and Active-pixel Vision Sensor (DAVIS) enhances information diversity by combining a standard camera with an event-based camera. However, current methods analyze event streams synchronously, contradicting their nature and introducing noise. To address this, most approaches accumulate events within a time interval to create synchronous frames, wasting sensitive intensity changes. This paper introduces a novel neural asynchronous approach for event stream analysis. Our method asynchronously extracts dynamic information by leveraging historical motion information and critical features of grayscale frames. Extensive experiments demonstrate our model’s significant improvements over state-of-the-art baselines.

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Published

03-09-2024

How to Cite

Zeyu Wang, Zong Cheng Chu, Minghao Chen, Yiqian Zhang, & Rui Yang. (2024). An Asynchronous LLM Architecture for Event Stream Analysis with Cameras. Social Science Journal for Advanced Research, 4(5), 10–17. https://doi.org/10.5281/zenodo.13639724