RSC-GestureNet: Reliability-Aware Selective Causal Recognition of Chinese Traffic Police Gestures
Traffic police gestures are safety-critical perception cues for autonomous driving. A deployable recognizer must infer commands causally from continuous full-frame video, remain stable around transitional arm motion, and avoid over-trusting corrupted pose measurements. This study presents RSC-GestureNet, a reliability-aware selective causal recognizer, for Chinese traffic police gestures. The model treats pose confidence as a first-class signal: unreliable joints are down weighted during graph reasoning, temporal evidence is aggregated causally, and calibrated predictions are selectively emitted through a reliability-aware inference rule. We further introduce CTPGesture-C, a reproducible ...
| Authors | Cheng Li, Renjun Gao, Boyi Fu |
|---|---|
| Primary category | cs.CV |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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