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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月16日

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  • Jie Chu,
  • Runze Zhang,
  • Chu Yang,
  • Zongyou Yu,
  • Zongtao Bu,
  • Haotian Liu,
  • Florian Röhrbein,
  • Alois Knoll,
  • Guang Chen,
  • Changjun Jiang

摘要

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.