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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | https://arxiv.org/abs/2211.00198 |
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| _version_ | 1866915570142674944 |
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| author | Pfrommer, Bernd |
| author_facet | Pfrommer, Bernd |
| contents | Due to their high temporal resolution and large dynamic range, event cameras are uniquely suited for the analysis of time-periodic signals in an image. In this work we present an efficient and fully asynchronous event camera algorithm for detecting the fundamental frequency at which image pixels flicker. The algorithm employs a second-order digital infinite impulse response (IIR) filter to perform an approximate per-pixel brightness reconstruction and is more robust to high-frequency noise than the baseline method we compare to. We further demonstrate that using the falling edge of the signal leads to more accurate period estimates than the rising edge, and that for certain signals interpolating the zero-level crossings can further increase accuracy. Our experiments find that the outstanding capabilities of the camera in detecting frequencies up to 64kHz for a single pixel do not carry over to full sensor imaging as readout bandwidth limitations become a serious obstacle. This suggests that a hardware implementation closer to the sensor will allow for greatly improved frequency imaging. We discuss the important design parameters for fullsensor frequency imaging and present Frequency Cam, an open-source implementation as a ROS node that can run on a single core of a laptop CPU at more than 50 million events per second. It produces results that are qualitatively very similar to those obtained from the closed source vibration analysis module in Prophesee's Metavision Toolkit. The code for Frequency Cam and a demonstration video can be found at https://github.com/ros-event-camera/frequency_cam |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_00198 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Frequency Cam: Imaging Periodic Signals in Real-Time Pfrommer, Bernd Computer Vision and Pattern Recognition Neural and Evolutionary Computing I.4.0 Due to their high temporal resolution and large dynamic range, event cameras are uniquely suited for the analysis of time-periodic signals in an image. In this work we present an efficient and fully asynchronous event camera algorithm for detecting the fundamental frequency at which image pixels flicker. The algorithm employs a second-order digital infinite impulse response (IIR) filter to perform an approximate per-pixel brightness reconstruction and is more robust to high-frequency noise than the baseline method we compare to. We further demonstrate that using the falling edge of the signal leads to more accurate period estimates than the rising edge, and that for certain signals interpolating the zero-level crossings can further increase accuracy. Our experiments find that the outstanding capabilities of the camera in detecting frequencies up to 64kHz for a single pixel do not carry over to full sensor imaging as readout bandwidth limitations become a serious obstacle. This suggests that a hardware implementation closer to the sensor will allow for greatly improved frequency imaging. We discuss the important design parameters for fullsensor frequency imaging and present Frequency Cam, an open-source implementation as a ROS node that can run on a single core of a laptop CPU at more than 50 million events per second. It produces results that are qualitatively very similar to those obtained from the closed source vibration analysis module in Prophesee's Metavision Toolkit. The code for Frequency Cam and a demonstration video can be found at https://github.com/ros-event-camera/frequency_cam |
| title | Frequency Cam: Imaging Periodic Signals in Real-Time |
| topic | Computer Vision and Pattern Recognition Neural and Evolutionary Computing I.4.0 |
| url | https://arxiv.org/abs/2211.00198 |