Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception
Researchers have developed Edge-TSR, a new system for continuous edge inference designed for roadside perception tasks on resource-constrained hardware like the NVIDIA Jetson Orin Nano. The system addresses deployment challenges such as temporal instability and thermal throttling, which are often overlooked by traditional benchmarks. Edge-TSR integrates detection, tracking, classification, and a lightweight temporal stabilization mechanism, showing a significant performance degradation of 20-30% compared to static-image evaluations. The system demonstrates sustained real-time performance, achieving 16.18 FPS during a 55-minute real-world deployment without cloud offload, while maintaining safe thermal limits. AI
IMPACT This research highlights the need for deployment-aware evaluation and temporal stabilization for edge AI systems, potentially improving real-world performance and reliability.
