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About this paper
Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, Baocai Yin, Xin Yang
AFNet combines conventional frame and event camera data to achieve robust single object tracking at high frame rates, demonstrating performance up to 240Hz and outperforming existing state-of-the-art trackers on dedicated datasets like FE240hz and VisEvent.
Key insights
- Combining low-frequency, texture-rich frame data with high-frequency, motion-sensitive event data is essential for robust high frame rate tracking.
- Effective alignment of heterogeneous data, differing in visual characteristics and temporal rates, is critical for successful multi-modal fusion.
- An adaptive fusion strategy that mutually enhances features between modalities is superior for robust performance across varying challenging conditions.
Summary by alphaXiv.
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