Evaluation of event-based algorithms for optical flow with ground-truth from inertial measurement sensor
| Autor: | Tobias Delbrück, Bodo Rueckauer |
|---|---|
| Přispěvatelé: | University of Zurich, Delbruck, Tobi |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Neuromorphic
AER Vision sensor Benchmarks Optical flow Inertial measurement unit DVS Silicon retina Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology lcsh:RC321-571 0202 electrical engineering electronic engineering information engineering Computer vision lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research 10194 Institute of Neuroinformatics Ground truth Pixel business.industry General Neuroscience 020208 electrical & electronic engineering Frame (networking) 2800 General Neuroscience Filter (signal processing) Flow (mathematics) 570 Life sciences biology 020201 artificial intelligence & image processing Artificial intelligence business Algorithm Rotation (mathematics) Neuroscience |
| Zdroj: | Frontiers in Neuroscience, 10 Frontiers in Neuroscience Frontiers in Neuroscience, Vol 10 (2016) |
| ISSN: | 1662-453X 1662-4548 |
| Popis: | In this study we compare nine optical flow algorithms that locally measure the flow normal to edges according to accuracy and computation cost. In contrast to conventional, frame-based motion flow algorithms, our open-source implementations compute optical flow based on address-events from a neuromorphic Dynamic Vision Sensor (DVS). For this benchmarking we created a dataset of two synthesized and three real samples recorded from a 240 × 180 pixel Dynamic and Active-pixel Vision Sensor (DAVIS). This dataset contains events from the DVS as well as conventional frames to support testing state-of-the-art frame-based methods. We introduce a new source for the ground truth: In the special case that the perceived motion stems solely from a rotation of the vision sensor around its three camera axes, the true optical flow can be estimated using gyro data from the inertial measurement unit integrated with the DAVIS camera. This provides a ground-truth to which we can compare algorithms that measure optical flow by means of motion cues. An analysis of error sources led to the use of a refractory period, more accurate numerical derivatives and a Savitzky-Golay filter to achieve significant improvements in accuracy. Our pure Java implementations of two recently published algorithms reduce computational cost by up to 29% compared to the original implementations. Two of the algorithms introduced in this paper further speed up processing by a factor of 10 compared with the original implementations, at equal or better accuracy. On a desktop PC, they run in real-time on dense natural input recorded by a DAVIS camera. Frontiers in Neuroscience, 10 ISSN:1662-453X ISSN:1662-4548 |
| Databáze: | OpenAIRE |
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