Bridging the latency gap with a continuous stream evaluation framework in event-driven perception

Neuromorphic vision systems process continuous event streams and offer transformative potential for real-time applications. However, their evaluation remains tethered to methodologies from RGB imaging. These approaches convert asynchronous event streams into synchronized frames and ignore perception latency, creating a critical gap between benchmarks and real-world performance. To address this, we introduce the STream-based lAtency-awaRe Evaluation (STARE) framework. STARE integrates two core components: Continuous Sampling, maximizing model throughput to reduce the impact of latency, and Latency-Aware Evaluation, quantifying latency-induced online accuracy. To rigorously validate STARE, we developed ESOT500, a high-dynamic object tracking dataset with 500 Hz annotations. Experiments reveal that latency severely degrades online accuracy by over 50%. We further introduce two model enhancement strategies: Asynchronous Tracking, a fast-slow architecture that boosts model throughput, and Context-Aware Sampling, which dynamically adapts input to handle low event density cases. Overall, our work bridges the latency gap between models’ theoretical potential and real-world deployment. In neuromorphic vision, frame-based benchmarks substantially overestimate performance. Here, the authors introduce latency-aware evaluation framework, bridging the sim-to-real gap.