论文标题
通过三重匹配的快速基于事件的光流估计
Fast Event-based Optical Flow Estimation by Triplet Matching
论文作者
论文摘要
事件摄像机是新型的生物风格传感器，比传统摄像机（低潜伏期，高动态范围，低功率等）具有优势。在事件数据包上使用的光流估计方法以取得准确的速度，而事件（增量）方法具有很强的假设，并且尚未在量化现场进展的常见基准上进行测试。为了在资源受限的设备上应用，开发快速，轻巧和准确的光流算法很重要。这项工作利用了神经科学的见解，并提出了基于三重态匹配的新型光流估计方案。公开可用基准测试的实验证明了其处理复杂场景的能力，其结果与先前的基于数据包的算法相当。此外，所提出的方法在标准CPU上达到了最快的执行时间（> 10 kHz），因为它仅需要三个事件。我们希望我们的研究能为现实情况下的实时，增量运动估计方法和应用打开大门。
Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time (> 10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios.
