Graph-based Asynchronous Event Processing for Rapid Object Recognition

ICCV 2021

1State Key Lab of CAD & CG, Zhejiang University,


SlideGCN performs incremental graph convolution and can efficiently process data event-by-event.

Different from traditional video cameras, event cameras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes. In this paper, we introduce a novel graph-based framework for event cameras, namely SlideGCN. Unlike some recent graph-based methods that use groups of events as input, our approach can efficiently process data event-by-event, unlock the low latency nature of events data while still maintaining the graph’s structure internally. For fast graph construction, we develop a radius search algorithm, which better exploits the partial regular structure of event cloud against k-d tree based generic methods. Experiments show that our method reduces the computational complexity up to 100 times with respect to current graph-based methods while keeping state-of-the-art performance on object recognition. Moreover, we verify the superiority of event-wise processing with our method. When the state becomes stable, we can give a prediction with high confidence, thus making an early recognition.


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  title={Graph-based Asynchronous Event Processing for Rapid Object Recognition},
  author={Li Yijin and Zhou Han and Yang Bangbang and Cui Zhaopeng and Bao Hujun and Zhang Guofeng},
  booktitle={International Conference on Computer Vision (ICCV)},
  month = {October},
  year = {2021},