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 cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur, and staying silent otherwise. Modern event cameras have microsecond temporal resolution, 120 dB dynamic range, and less under/overexposure and motion blur[4][5] than frame cameras.

Functional description

Event cameras contain pixels that independently respond to changes in brightness as they occur.[4] Each pixel stores a reference brightness level, and continuously compares it to the current level of brightness. If the difference in brightness exceeds a preset threshold, that pixel resets its reference level and generates an event: a discrete packet of information containing the pixel address and timestamp. Events may also contain the polarity (increase or decrease) of a brightness change, or an instantaneous measurement of the current level of illumination.[6] Thus, event cameras output an asynchronous stream of events triggered by changes in scene illumination.

Comparison of the data produced by an event camera and a conventional camera.
Comparison of the data produced by an event camera and a conventional camera.
Typical characteristics of image sensors
Sensor Dynamic

range (dB)


framerate* (fps)


resolution (MP)


consumption (mW)

Human eye 30–40 200-300 - 10[7]
High-end DSLR camera (Nikon D850) 44.6[8] 120 2–8 -
Ultrahigh-speed camera (Phantom v2640)[9] 64 12,500 0.3–4 -
Event camera[10] 120 1,000,000 0.1–0.2 30

*Indicates temporal resolution since human eyes and event cameras do not output frames.


While all event cameras respond to local changes in brightness, there are a few variants. Temporal contrast sensors (like the pioneering DVS[4] (Dynamic Vision Sensor) or the sDVS[11] (sensitive-DVS)) produce events that indicate polarity (increase or decrease in brightness), while temporal image sensors[6] indicate the instantaneous intensity with each event. The DAVIS[12] (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).

Event cameras
Name Event output Image frames Color IMU Manufacturer Commercially available
DVS128[4] Polarity No No No Inivation No
sDVS128[11] Polarity No No No CSIC No
DAVIS240[12] Polarity Yes No Yes Inivation Yes
DAVIS346[13] Polarity Yes No Yes Inivation Yes
SEES[14] Polarity Yes No Yes Insightness Yes
SilkyEvCam[15] Polarity No No No Century Arks Yes
Samsung DVS[16] Polarity No No Yes Samsung No
Onboard[6] Polarity No No Yes Prophesee Yes
Celex[17] Intensity Yes No Yes CelePixel Yes
IMX636[18] Intensity Yes No Yes Sony / Prophesee Yes

Retinomorphic sensors

Main article: Retinomorphic sensor

Left: schematic cross-sectional diagram of photosensitive capacitor. Center: circuit diagram of retinomorphic sensor, with photosensitive capacitor at top. Right: Expected transient response of retinomorphic sensor to application of constant illumination.
Left: schematic cross-sectional diagram of photosensitive capacitor. Center: circuit diagram of retinomorphic sensor, with photosensitive capacitor at top. Right: Expected transient response of retinomorphic sensor to application of constant illumination.

Another class of event sensors are so-called retinomorphic sensors. While the term retinomorphic has been used to describe event sensors generally,[19][20] in 2020 it was adopted as the name for a specific sensor design based on a resistor and photosensitive capacitor in series.[21] These capacitors are distinct from photocapacitors, which are used to store solar energy,[22] and are instead designed to change capacitance under illumination. They are therefore expected to charge / discharge slightly when the capacitance is changed, but otherwise remain in equilibrium. When the photosensitive capacitor is placed in series with a resistor, and an input voltage is applied across the circuit, the result is a sensor which outputs a voltage when the light intensity changes, but otherwise outputs no signal.

Unlike other event sensors (which typically consist of a photodiode and some other circuit elements), these retinomorphic sensors produce the signal inherently by design. They can hence be considered a single device which produces the same result as a small circuit in other event cameras. Retinomorphic sensors have to-date only been studied in a research environment, but are hoped to have applications in object recognition, autonomous vehicles, and robotics.[23][24][25][26]


A pedestrian runs in front of car headlights at night. Left: image taken with a conventional camera exhibits severe motion blur and underexposure. Right: image reconstructed by combining the left image with events from an event camera.[27]
A pedestrian runs in front of car headlights at night. Left: image taken with a conventional camera exhibits severe motion blur and underexposure. Right: image reconstructed by combining the left image with events from an event camera.[27]

Image Reconstruction

Image reconstruction from events has the potential to create images and video with high dynamic range, high temporal resolution and minimal motion blur. Image reconstruction can be achieved using temporal smoothing, e.g. high-pass or complementary filter.[27] Alternative methods include optimization[28] and gradient estimation[29] followed by Poisson integration.

Spatial convolutions

The concept of spatial event-driven convolution was initially postulated in 1999[30] (before the DVS invention), but later generalized during EU project CAVIAR[31] (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.[32] Extension to multi-kernel event-driven convolutions[33] allows for event-driven deep convolutional neural networks.[34]

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 very little information[35] and do not contain useful visual features like texture and color that are essential.[36] These tasks become further challenging in the scenario of a moving camera[35] 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[37][38] and using traditional clustering algorithms.[39][40][36][41]

See also


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