Introduces the first large-scale event-based HAR benchmark under low-light and 6-DoF shaking conditions with IMU data, and an EIS-HAR pipeline using non-linear warping for motion compensation plus a four-stage hybrid network that outperforms prior methods on three datasets.
Event-based motion segmentation with spatio- temporal graph cuts.IEEE transactions on neural networks and learning systems, 34(8):4868–4880, 2021
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DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions
Introduces the first large-scale event-based HAR benchmark under low-light and 6-DoF shaking conditions with IMU data, and an EIS-HAR pipeline using non-linear warping for motion compensation plus a four-stage hybrid network that outperforms prior methods on three datasets.