Fast Event-based Optical Flow Estimation by Triplet Matching
This work addresses the need for fast, lightweight optical flow algorithms for resource-constrained devices, though it is incremental as it builds on existing event-based methods.
The paper tackles optical flow estimation for event cameras by proposing a triplet matching method, achieving comparable accuracy to prior packet-based algorithms and the fastest execution time (>10 kHz) on standard CPUs.
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.