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Fast Event-Based Optical Flow Estimation by Triplet Matching
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AI
DOI：10.1109/LSP.2023.3234800.png)
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Abstract
En 中文
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.
Keywords:
Estimation
Optical flow
Signal processing algorithms
Cameras
Benchmark testing
Indexes
Neuroscience
Event cameras
optical flow
low latency
asynchronous sensor
neuroscience
robotics
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W