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About this paper
Yijin Li, Han Zhou, Bangbang Yang, Ye Zhang, Zhaopeng Cui, Hujun Bao, Guofeng Zhang
Researchers at Zhejiang University developed SlideGCN, a graph-based framework for event camera object recognition that processes events incrementally, preserving their native asynchronous nature and achieving high accuracy with improved computational efficiency. This method maintains event temporal fidelity while reducing computational costs for real-time applications.
Key insights
- An incremental graph convolution (slide convolution) efficiently updates node features by recomputing only active nodes and their neighbors, reducing computational complexity significantly.
- A pixel queue-based radius search algorithm exploits the 2D pixel grid origin of event data, outperforming traditional k-d tree methods for graph construction.
- A state-aware module predicts prediction stability, enabling 'early recognition' by stopping processing once sufficient information for a reliable prediction is gathered.
Summary by alphaXiv.
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