Event camera explained

An event camera, also known as a neuromorphic camera,[1] silicon retina[2] or dynamic vision sensor,[3] is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional (frame) cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur, and staying silent otherwise.

Functional description

Event camera pixels independently respond to changes in brightness as they occur.[4] Each pixel stores a reference brightness level, and continuously compares it to the current brightness level. If the difference in brightness exceeds a threshold, that pixel resets its reference level and generates an event: a discrete packet that contains the pixel address and timestamp. Events may also contain the polarity (increase or decrease) of a brightness change, or an instantaneous measurement of the illumination level,[5] depending on the specific sensor model. Thus, event cameras output an asynchronous stream of events triggered by changes in scene illumination.Event cameras typically report timestamps with a microsecond temporal resolution, 120 dB dynamic range, and less under/overexposure and motion blur[6] than frame cameras. This allows them to track object and camera movement (optical flow) more accurately. They yield grey-scale information. Initially (2014), resolution was limited to 100 pixels. A later entry reached 640x480 resolution in 2019. Because individual pixels fire independently, event cameras appear suitable for integration with asynchronous computing architectures such as neuromorphic computing. Pixel independence allows these cameras to cope with scenes with brightly and dimly lit regions without having to average across them.[7] It is important to note that while the camera reports events with microsecond resolution, the actual temporal resolution (or, alternatively, the bandwidth for sensing) is in the order of tens of microseconds to a few miliseconds - depending on signal contrast, lighting conditions and sensor design.[8]

Typical image sensor characteristics!Sensor!Dynamicrange (dB)!Equivalent framerate (fps)!Spatialresolution (MP)!Powerconsumption (mW)
Human eye30–40200-300*-10[9]
High-end DSLR camera (Nikon D850)44.6[10] 1202–8-
Ultrahigh-speed camera (Phantom v2640)[11] 6412,5000.3–4-
Event camera[12] 12050,000 - 300,000**0.1–130

Types

Temporal contrast sensors (such as DVS (Dynamic Vision Sensor), or sDVS[13] (sensitive-DVS)) produce events that indicate polarity (increase or decrease in brightness), while temporal image sensors indicate the instantaneous intensity with each event. The DAVIS[14] (Dynamic and Active-pixel Vision Sensor) contains a global shutter active pixel sensor (APS) in addition to the dynamic vision sensor (DVS) that shares the same photosensor array. Thus, it has the ability to produce image frames alongside events. Many event cameras additionally carry an inertial measurement unit (IMU).

Retinomorphic sensors

See main article: Retinomorphic sensor. Another class of event sensors are so-called retinomorphic sensors. While the term retinomorphic has been used to describe event sensors generally,[15] [16] in 2020 it was adopted as the name for a specific sensor design based on a resistor and photosensitive capacitor in series.[17] These capacitors are distinct from photocapacitors, which are used to store solar energy,[18] and are instead designed to change capacitance under illumination. They charge/discharge slightly when the capacitance is changed, but otherwise remain in equilibrium. When a photosensitive capacitor is placed in series with a resistor, and an input voltage is applied across the circuit, the result is a sensor that outputs a voltage when the light intensity changes, but otherwise does not.

Unlike other event sensors (typically a photodiode and some other circuit elements), these sensors produce the signal inherently. They can hence be considered a single device that produces the same result as a small circuit in other event cameras. Retinomorphic sensors have to-date only been studied in a research environment.[19] [20] [21] [22]

Algorithms

Image reconstruction

Image reconstruction from events has the potential to create images and video with high dynamic range, high temporal resolution and reduced motion blur. Image reconstruction can be achieved using temporal smoothing, e.g. high-pass or complementary filter. Alternative methods include optimization[23] and gradient estimation[24] followed by Poisson integration.

Spatial convolutions

The concept of spatial event-driven convolution was postulated in 1999[25] (before the DVS), but later generalized during EU project CAVIAR[26] (during which the DVS was invented) by projecting event-by-event an arbitrary convolution kernel around the event coordinate in an array of integrate-and-fire pixels.[27] Extension to multi-kernel event-driven convolutions[28] allows for event-driven deep convolutional neural networks.[29]

Motion detection and tracking

Segmentation and detection of moving objects viewed by an event camera can seem to be a trivial task, as it is done by the sensor on-chip. However, these tasks are difficult, because events carry little information[30] and do not contain useful visual features like texture and color.[31] These tasks become further challenging given a moving camera, because events are triggered everywhere on the image plane, produced by moving objects and the static scene (whose apparent motion is induced by the camera’s ego-motion). Some of the recent approaches to solving this problem include the incorporation of motion-compensation models[32] [33] and traditional clustering algorithms.[34] [35] [36]

Potential applications

Potential applications include most tasks classically fitting conventional camera, but with emphasis on machine vision tasks (such as object recognition, autonomous vehicles, and robotics.). The US military is considering infrared and other event cameras because of their lower power consumption and reduced heat generation.[7]

Considering the advantages the event camera possesses, compared to conventional image sensors, it is considered fitting for applications requiring low power consumption, low latency, and difficulty to stabilize camera line of sight. These applications include the aforementioned autonomous systems, but also space imaging, security, defense and industrial monitoring. It is notable that while research into color sensing with event cameras is underway,[37] it is not yet convenient for use with applications requiring color sensing.

See also

Notes and References

  1. Li . Hongmin . Liu . Hanchao . Ji . Xiangyang . Li . Guoqi . Shi . Luping . CIFAR10-DVS: An Event-Stream Dataset for Object Classification . Frontiers in Neuroscience . 2017 . 11 . 309 . 10.3389/fnins.2017.00309 . 28611582 . English . 1662-453X. 5447775 . free .
  2. Sarmadi . Hamid . Muñoz-Salinas . Rafael . Olivares-Mendez . Miguel A. . Medina-Carnicer . Rafael . Detection of Binary Square Fiducial Markers Using an Event Camera . IEEE Access . 2021 . 9 . 27813–27826 . 10.1109/ACCESS.2021.3058423 . 2169-3536. 2012.06516 . 2021IEEEA...927813S . 228375825 .
  3. Book: Liu . Min . Delbruck . Tobi . 2017 IEEE International Symposium on Circuits and Systems (ISCAS) . Block-matching optical flow for dynamic vision sensors: Algorithm and FPGA implementation . May 2017 . 1–4 . 10.1109/ISCAS.2017.8050295 . 1706.05415 . 978-1-4673-6853-7 . 2283149 . 27 June 2021.
  4. Lichtsteiner . P. . Posch . C. . Delbruck . T. . February 2008 . A 128×128 120 dB 15μs Latency Asynchronous Temporal Contrast Vision Sensor . IEEE Journal of Solid-State Circuits . 43 . 2 . 566–576 . 2008IJSSC..43..566L . 10.1109/JSSC.2007.914337 . 0018-9200 . 6119048 . 2019-12-06 . 2021-05-03 . https://web.archive.org/web/20210503085033/https://www.zora.uzh.ch/id/eprint/17629/1/Lichtsteiner_Latency_V.pdf . dead .
  5. Posch . C. . Matolin . D. . Wohlgenannt . R. . January 2011 . A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS . IEEE Journal of Solid-State Circuits . 46 . 1 . 259–275 . 2011IJSSC..46..259P . 10.1109/JSSC.2010.2085952 . 0018-9200 . 21317717.
  6. Web site: Longinotti. Luca. Product Specifications. 2019-04-21. iniVation. 2019-04-02. https://web.archive.org/web/20190402163200/https://inivation.com/support/product-specifications/. dead.
  7. News: 2022-01-29. A new type of camera. The Economist. 2022-02-02. 0013-0613.
  8. Hu . Yuhuang . v2e: From Video Frames to Realistic DVS Events . 2021-04-19 . 2006.07722 . Liu . Shih-Chii . Delbruck . Tobi. cs.CV .
  9. Skorka. Orit. 2011-07-01. Toward a digital camera to rival the human eye. Journal of Electronic Imaging. 20. 3. 033009–033009–18. 10.1117/1.3611015. 1017-9909. 2011JEI....20c3009S. 9340738 .
  10. Web site: Nikon D850 : Tests and Reviews DxOMark. DxO. www.dxomark.com. 2019-04-22.
  11. Web site: Phantom v2640. www.phantomhighspeed.com. 2019-04-22.
  12. Web site: Product Specifications. Longinotti. Luca. iniVation. 2019-04-22. 2019-04-02. https://web.archive.org/web/20190402163200/https://inivation.com/support/product-specifications/. dead.
  13. Serrano-Gotarredona . T. . Linares-Barranco . B. . March 2013 . A 128x128 1.5% Contrast Sensitivity 0.9% FPN 3μs Latency 4mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Amplifiers . IEEE Journal of Solid-State Circuits . 48 . 3 . 827–838 . 2013IJSSC..48..827S . 10.1109/JSSC.2012.2230553 . 0018-9200 . 6686013.
  14. Brandli. C.. Berner. R.. Yang. M.. Liu. S.. Delbruck. T.. October 2014. A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor. IEEE Journal of Solid-State Circuits. 49. 10. 2333–2341. 10.1109/JSSC.2014.2342715. 0018-9200. 2014IJSSC..49.2333B. free.
  15. Book: Boahen, K.. Proceedings of Fifth International Conference on Microelectronics for Neural Networks . Retinomorphic vision systems . 1996. https://ieeexplore.ieee.org/document/493766. 2–14. 10.1109/MNNFS.1996.493766. 0-8186-7373-7. 62609792.
  16. Posch. Christoph. Serrano-Gotarredona. Teresa. Linares-Barranco. Bernabe. Delbruck. Tobi. 2014. Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output. Proceedings of the IEEE. 102. 10. 1470–1484. 10.1109/JPROC.2014.2346153. 11441/102353. 11513955. 1558-2256. free.
  17. Trujillo Herrera. Cinthya. Labram. John G.. 2020-12-07. A perovskite retinomorphic sensor. Applied Physics Letters. 117. 23. 233501. 10.1063/5.0030097. 2020ApPhL.117w3501T. 230546095. 0003-6951. free.
  18. Miyasaka. Tsutomu. Murakami. Takurou N.. 2004-10-25. The photocapacitor: An efficient self-charging capacitor for direct storage of solar energy. Applied Physics Letters. 85. 17. 3932–3934. 10.1063/1.1810630. 2004ApPhL..85.3932M. 0003-6951.
  19. Web site: 2021-01-18. Perovskite sensor sees more like the human eye. 2021-10-28. Physics World. en-GB.
  20. Web site: Simple Eyelike Sensors Could Make AI Systems More Efficient. 2021-10-28. Inside Science. 8 December 2020 . en.
  21. Web site: Hambling. David. AI vision could be improved with sensors that mimic human eyes. 2021-10-28. New Scientist. en-US.
  22. Web site: An eye for an AI: Optic device mimics human retina. 2021-10-28. BBC Science Focus Magazine. en.
  23. Book: Pan. Liyuan. Scheerlinck. Cedric. Yu. Xin. Hartley. Richard. Liu. Miaomiao. Dai. Yuchao. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 2019. Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera. Long Beach, CA, USA. IEEE. 6813–6822. 10.1109/CVPR.2019.00698. 978-1-7281-3293-8. 1811.10180. 53749928.
  24. Scheerlinck. Cedric. Barnes. Nick. Mahony. Robert. April 2019. Asynchronous Spatial Image Convolutions for Event Cameras. IEEE Robotics and Automation Letters. 4. 2. 816–822. 1812.00438. 10.1109/LRA.2019.2893427. 59619729. 2377-3766.
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  37. Web site: CED: Color Event Camera Dataset . 2024-04-08 . rpg.ifi.uzh.ch.

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