Feedback control of event cameras
Tobi Delbruck, Rui Graca, Marcin Paluch
Inst. of Neuroinformatics, UZH-ETH Zurich, Switzerland
Corresponding author: tobi@ini.uzh.ch

brightness

Brightness change event stream
Thresholdtime

Refractory period
Event rate

Response

Bandwidth

Frequency (Hz)Statistics
event rate
signal and noise

Eventse(t,x,y,p)Control policy

Fig. 1: Event camera feedback control dynamically modifies the threshold, bandwidth, and refractory period
during operation to regulate the DVS
output.

Activity

Abstract
Dynamic vision sensor event cameras produce a variabledata rate stream of brightness change events. Event production at the pixel level is controlled by threshold, bandwidth,
and refractory period bias current parameter settings. Biases must be adjusted to match application requirements
and the optimal settings depend on many factors. As a firststep towards automatic control of biases, this paper proposes fixed-step feedback controllers that use measurementsof event rate and noise. The controllers regulate the event
rate within an acceptable range using threshold and refractory period control, and regulate noise using bandwidth
control. Experiments demonstrate model validity and feedback control.

1. Introduction
Dynamic vision sensor (DVS) event cameras [1], [2]such as those sold now by Inivation, Prophsee, Insightness, and Celex offer superior dynamic range and sparse,
quick output that makes them useful for vision under uncontrolled lighting encountered in surveillance and mobile
applications. The high-quality output from standard cameras relies on decades of work to automatically optimize
parameters such as column converter gain, noise filtering,
exposure time, white balance, FPN correction, white/dark
pixel removal, aperture control, and focus control [3]. Thesealgorithms provide high quality output from smartphone,surveillance, and automotive cameras. There has been lit-

tle work to dynamically optimize DVS output. With the increasing use of DVS in uncontrolled environments it is agood time to develop feedback control algorithms to optimize the DVS operating parameters. This study can guidethe development of future DVS event cameras and theirfirmware and software frameworks.
Fig. 1 illustrates the control of threshold, refractory period, and bandwidth of a DVS camera based on statisticalmeasurements of event rate and noise in the event stream.
The paper contributions and outline are:
1. An overview of the targets for control (Sec. 2).
2. Models of DVS pixel bandwidth. threshold, and refractory period as functions of the pixel bias currents(Sec. 3).
3. Fixed-step regulators for DVS biases targeting event
rate and noise bounds (Sec. 4), and an online estimateof signal and noise (Sec. 4.2.1).
4. Experiments with a DVS camera that demonstratemodel validity and control (Sec. 5).
Symbols used throughout the paper are listed in Table 1.

1.1. Prior work
Very high DVS event rates cause problems such as saturating the readout bus or hindering real time event processing. To overcome this, several schemes aiming to limit
event rate were proposed. Dropping DVS events that cannot be transmitted in time to the host computer by USB has
been part of event sensor interface logic since [4]. Ref. [5]proposed an approach where the algorithm processing thepackets used feedback control based on processing latency
to adaptively adjust software algorithm processing costs, byadjusting the event batch size or fraction of dropped events.
A related approach [6] also proposed software to ensure thatdata can be processed in real time. They randomly discard afraction of the events under feedback control to ensure realtime operation. In addition, they adaptively size the processed event packets to match the processing rate.
Recent industrial event cameras include on chip someform of control of the event readout or DVS frame sampling
rate [7]–[9]. By contrast, the DVS camera control describedhere controls the analog parameters of DVS pixel operation
rather than the camera’s event output bus or software processing algorithm. This way, it can control the event generation at the pixel level and optimize the pixel operation to aparticular application or environment.
There appear to be no publications reporting performance of algorithms for automatically controlling thebias current parameters of DVS event cameras. Litzenberger [10] counted cars with a embedded camera using
the first generation 64x64 pixel DVS chip [11]. They used
two sets of voltage biases optimized by hand for daytime
and nighttime car counting where the thresholds were increased for nighttime (based on clock and calendar) to detect only car headlights. A DVS threshold control algorithm
was posted online around 20111 . Patent applications that appear to be based on this algorithm were filed in 2017 and2018 [12], [13]. A continuation in 2020 [14] extended oneof the applications to vary the global sampling rate basedon event rate. However, none of these publications reportexperimental results about the effect of automatic bias control.

2. Overview of control aims
DVS event camera systems face at least two competing
constraints: Adjusting biases to result in more events usually means more informative data, but also more computing
cost and more noise. We will assume there are two primaryobjectives of bias control:
Objective 1 is to limit the event rate within acceptablebounds. We will assume that there is a largest acceptable
rate of events that the DVS can output without bus saturation or that the system can process in real time. We call it thehigh rate limit RH . Likewise, there is a low rate limit RL ,which represents the quiescent event rate that the system
can afford to process all the time. The job of the bias controller is to regulate the DVS event rate within these bounds.
Sec. 4.1 explains our approach for regulating this bound.
Objective 2 is to optimize the signal and noise. It is
important to realize it is not to maximize the signal to noise
ratio, since this objective is easily achieved by removing
almost all the noise by using a high threshold and small
bandwidth—doing this also removes much of the informa1 DVS128BiasController.java on SourceForge.

Symbol Default
Description
Biases–Sec. 3
Ipr 1nA
Photoreceptor bias
Isf 25pA
Source-follower bias
Id 20nA
Differencing amp bias
Ion 1.3uA
ON comparator bias
Ioff 300pA
OFF comparator bias
Irefr 5nA
Refractory period bias
Event threshold–Sec. 3.2
θ 0.28 e-fold/ev Temporal contrast thresholdΣ 3.6 ev/e-fold
Sensitivity
Bandwidth–Sec. 3.3
Bpr –Photoreceptor bandwidth
Refractory period–Sec. 3.4∆refr ≈1us
Refractory period
Tweaks–Sec. 3.1 with [1/Tmin , Tmax ] valuesTθ [1/10,10]
Threshold tweak
TBW [1/30,30]
Bandwidth tweak
Trefr [1/100,8]
Refractory period tweak
Fixed-step control–Sec. 4∆bb 0.1
Tweak change.
H 1.5
Hysteresis; see Fig. 3.
tignore 1s
Time to ignore events.
tbb 2sMinimum control interval.
Event rate bounding and limiting–Secs. 4.1.1,4.1.2
RH 300kHz
Upper event rate limit
RL 100kHz
Lower event rate limit
Signal and Noise–Sec. 4.2.2
RI –Input (raw) event rate
RN –Noise event rate
RS –Signal (cleaned) event rate
RNL 0.5 Hz/pix
Limit for noise
RS-N –
Normalized S-N difference
Table 1: Symbols with values used in this paper.
tive signal events. Sec. 4.2.1 explains how we can measure
the noise event rate RN , and from it compute a signal versus
noise quantity called RS-N , Sec. 5.3.3 shows experimental
data of RS-N , and Sec. 5.3.3 demonstrates a controller that
regulates RN to remain close to a rate RNL .

3. DVS biases
Fig. 2 shows a schematic of the DVS pixel with the associated bias current sources and sinks. The currents are specified at the pixel array level by the chip’s bias generator.
Changing the bias currents changes the temporal contrast
event threshold θ, the photoreceptor bandwidth Bpr , andthe refractory period ∆refr between events.
The critical threshold for creating ON and OFF events
is controlled by the Id , Ion , and Ioff currents. The temporal bandwidth (speed) of the photoreceptor front end is

The θ magnitude can range over about [0.15, 0.5]. For thisrange of small θ, 1 + θ is approximately the relative change
of intensity to create an event. e.g., if θ = −0.2, then adecrease by a factor of about 0.8 creates an OFF event.
We will assume equal ON and OFF threshold, i.e. θ =θon = −θoff , to balance the ON and OFF event rates. We
define the temporal contrast sensitivity Σ:Σ=(2)

2 DVSTweaks in jAER

R=

Vsf

light
PD

I=Ip+Idark
A photoreceptor

C3

C2

B buffer

Vd
Id

(5)

ON

OFF

Ion

Ioff

C change amplifier D comparators

Vrefr
reset
acknowledge

E refractory period

Threshold θ

Vsf fast BW
slow BWθoffvoltageVp

Refractory
Irefr period ∆refr

C1

reset

lnI

Isf

R0
(Σ − Σmin ),Σ0 − Σminwhere R0 is the nominal event rate at the nominal sensivity
Σ0 (with Tθ = 0), and Σmin = 1/θmax is the minimum
sensitivity that triggers events. Secs. 5.2.1 show that thismodel accurately describes experimental results for a usefulrange of Σ. For large Σ, measurements suggest that noise
significantly contributes to the total event rate, making thelinear model no longer valid.

Bandwidth (Bpr)

Ipr

(4)

θon

F operating principle

Fig. 2: DVS pixel circuit with biases. (Adapted from [15].)OFF

Ion,off,Idθon,off = Aθ ln

1.
θΣ has units of events per e-fold intensity change.
We can use (2) to predict approximately how changing θwill change the DVS event rate, if we can assume that the
only thing that changes is the threshold. Naively, we can
expect that the event rate is simply scaled by Σ, because
the smaller the threshold, the more events will be produced.
However, if θ > θmax , where θmax is the highest contrast feature, then no events will be generated. If the scene contains a
distribution of temporal contrasts, then increasing sensitivity will gradually expose more and more parts of the scene
to create events. I.e., for low sensitivity, only the highest
contrast features will create events. Thus we expect that atleast within some range, the total signal event rate R will
follow the linear relationship3.2. Temporal contrast threshold
Ref. [16] showed that the event contrast thresholds θon,off
which are the natural log intensity change for creating ON
and OFF events are

(3)

OFF

where I0 is the nominal current. Limiting T to the range
T → [−1, 1] limits the current to the functional operation range I → I0 × [1/Tmin , Tmax ]. Table 1 lists the[1/Tmin , Tmax ] values used in this study, which were adjusted by the chip designers for functional DVS operation.

1.5C2≈ 1/15.5.
C1

OFF

The jAER developers realized that DVS users would
need a user friendly control panel to control otherwise cryptic bias currents, and so developed the notion of a dimensionless bias ‘tweak’ to control DVS bias currents2 . The
tweak interface - if properly optimised - automatically limits the range of bias modification, guarantees that the system stays within the functional range of its parameters, andhence assures safety of the controller. The DVS bias currents start from a nominal operating point (Table 1). A linear tweak results in an exponential scaling of bias currents
around the operating point. The threshold tweak Tθ , refractory period tweak Trefr , and bandwidth tweak TBW ,map T to the actual bias current(s) range(s), such that thecurrent I is computed from(eT ×ln Tmax if 0 ≤ T ≤ +1
I = I0 T ×ln Tmin
(1)e
if 0 > T ≥ −1AΘ ≈voltage

3.1. ‘Tweaking’ biases around operating point

where for the DAVIS346 camera used in this study

ON
ON
ON
ON
ON
ON

controlled by Ipr and Isf . The refractory period between
events—while the change amplifier is held in reset—is controlled by Irefr . Together, these 5 bias currents control themost important characteristics of DVS pixels. The larger thebias current, the higher the threshold or the faster the circuit.

Reset Level

Vd

time

3.3. Photoreceptor bandwidth
To first approximation, the DVS pixel photoreceptor circuit can be considered as a 2nd-order lowpass filter, where
the first stage cutoff is set by the photocurrent Ip together
with the photoreceptor bias current Ipr and the 2nd stage
cutoff is set by the source follower buffer bias current Isf .
For this paper, we assume that the overall bandwidth Bpr
around some operating point is a function f (Ipr , Isf ) of thetwo bias currents that increases monotonically with the currents:
Ipr Isf,)(6)
Bpr = Bpr0 f (Ipr0 Isf0
where the 0 subscript means the nominal value. The function is complex and depends strongly on the absolute lightintensity.
It is difficult to infer the effect of changing Bpr on anarbitrary event stream. We only know that increasing Bpr
will increase R until the bandwidth exceeds all input signal
frequencies, and will continue to increase R by increasing
noise events. Secs. 5.2.3 and 5.3.3 show experimental results.

3.4. Refractory period
Fig. 2 parts E&F shows how Irefr controls the dead time∆refr between events. During this refractory period, thechange amplifier is held in reset. The reset signal is pulled
low by the pixel acknowledge signal, and while reset rises
back up, the reset switch transistor Mr shorts across thechange amplifier output Vd and input Vr , balancing it. Thisrefractory period ∆refr is determined by∆refr =

C3
Irefr Vrefr(7)

where for the DAVIS346 used in this study, C3 ≈ 20fF and
Vrefr ≈ 0.5V; e.g. ∆refr = 10ms requires Irefr = 4pA.
During the refractory period, the change amplifier ignores changes in the photoreceptor output. The refractory
period sets a hard limit on the maximum event rate per pixel,
and it also reduces the effect of any input brightness change
by discarding the changes that occur during the refractory
period.
For a single pixel generating events at an instantaneous
rate r0 , the time interval between events is T0 = 1/r0 . A
simple model assumes that the refractory period ∆refr effectively increases the time between events to T0 + ∆refr ,resulting in the new rate r = 1/(T0 + ∆refr ). Averaged over
the whole pixel array, the total rate RS isZ ∞f (T )dT,(8)
RS = NtotalT+ ∆refr
0
where Ntotal is number of DVS pixels and f (T )dT gives
the fraction of pixels with time interval between events in

the range [T, T + dT ]. It follows that for any given f (T )(i.e., scene with constant statistical character) the refractory period monotonically limits the event rate. Experimental confirmation and a control experiment are presented in
Sections 5.2.2 and 5.3.2.

4. Controller design
The previous section described simple models of event
rate, signal and noise versus threshold, refractory period,and bandwidth biases. The main conclusion is that event
rate and noise vary monotonically with bias. Therefore,
all our controllers are very simple: They vary the relevant
tweak by fixed steps until the objective is obtained. In thispaper, we will call this fixed-step control. Fixed-step control
only requires monotonicity of the controller effect on the
DVS output. Fixed-step control can take only small steps toresult in accurate control, which makes it slow, but its easily obtained stable outcome may be an advantage for many
applications.

4.1. Controlling event rate
Fig 3 shows the design of the threshold and refractory
period controllers that are described in the following. We
measure event rate by a simple box filter that counts events
during a period (usually 300 ms) and divides the count by
the period.
4.1.1 Threshold control for bounding event rate
The threshold controller shown in Fig. 3A increases θ while
the event rate R is above the upper bound RH , and decreases it while R is below the lower bound RL . Every increase of Σ creates noise, so the control occurs at a rate
1/tbb . A hysteresis factor H holds the state until R crosses
the bound multiplied or divided by H.
4.1.2 Refractory control for limiting event rate
The refractory period controller shown in Fig. 3B limits the
maximum event rate. It increases ∆refr while the event rate
R is above the upper bound RH , and gradually decreases itback to the default value once R drops below RH . In contrast to threshold control, changing refractory period does
not produce much noise, but to reduce control actions, we
use the same limits on control rate and hysteresis as for thethreshold controller.

4.2. Controlling signal and noise with bandwidthFig. 4 illustrates the concept of bandwidth control for
passing as much signal as possible without creating too
much noise. As we increase bandwidth, more and moreof the high frequency signal components create brightnesschange events, but noise also increases. Eventually, thebandwidth is sufficient to capture all signal frequencies,

A: event rate bounding threshold controller

fromRL/Η ≤ R < ΗRH

R < RL/H

RS-N =

2: R is OK
R ≥ HRH

R < HRLR ≥ HRL

1: R too low

R≥

H

R H/

Decrease thresholdΤθ←Τθ−∆bb

R < RH/H

3: R too highIncrease thresholdΤθ←Τθ+∆bb

B: event rate limiting refractory period controller

2: R is OK

R < HRH

Decrease refractory period if above nominal valueΤrefr←Τrefr+∆bb if Τrefr<0

R ≥ HRH
R ≥ RH/H

R < RH/H

3: R too highIncrease refractory period
Τrefr←Τrefr−∆bb

C: noise regulating bandwidth controller

RN < RNL/H

RN

R NL

<H

RNL/Η ≤ RN < ΗRNL

2: RN is OK
RN ≥ HRNL

< RN

/H
L

RN ≥ HRNL
RN≥ RN
1: RN too low
RN
3: RN too highIncrease bandwidth:Τbw←Τbw+∆bb
Ignore events for next tignore/H

L

Decrease bandwidth:Τbw←Τbw−∆bb
Ignore events for next tignore

Fig. 3: Event rate (R) and noise (RN ) fixed-step controllers.
RS-N=(S-N)/(S+N)

RS − RN,
RS + RN(9)

where the event rates RS and RN are the rate of signal
and noise events. In this study, we measure RN by using
a correlation-based denoising algorithm called the Background Activity Filter [17]. If RI is the input event rate,and RN is the rate of events that are removed by denoising,then RS = RI − RN is the rate of denoised signal events.
RS-N ranges over [−1 : 1] and takes on a maximum value
when the difference between signal and noise rates is maximal. We adjusted the denoising correlation time to preserve
the signal and remove most of the noise even at the highest
bandwidth setting. Sec. 5.2.3 shows experimental results of
estimated RS-N .
4.2.2 Controlling bandwidth to regulate noise
A bandwidth controller might adjust bandwidth to maximize RS-N . However, the online measurement of RS-N requires very long averaging to remove the effect of fluctuating signal characteristics, so we did not attempt to develop
a controller. Instead, we developed the state-machine fixedstep controller to regulate noise illustrated in Fig. 3C. It operates similarly to the threshold and refractory period controllers, but its aim is to keep the noise event rate close to atarget RNL . Sec. 5.3.3 shows control experiment results.

5. Experimental results
This section reports our measurements and control experiment results. Results are based on source code DVSBiasController 3 using a prototype DAVIS346 camera frominiVation that uses a DAVIS sensor IC from our group [18].

5.1. Experimental setup
Event
rate

l
Signa

e

Nois

Bandwidth Bpr

Fig. 4: Concept for optimizing signal versus noise by controlling bandwidth.
while noise continues to increase. Sec. 5.2.3 shows an experiment for which an optimum setting exists, resulting in a
maximum difference between signal and noise.
4.2.1 Estimating signal and noise
We assume that signal and noise characteristics remain stable to allow their measurement. Furthermore, we assume
that a denoising algorithm can correctly filter out the DVS
background activity noise, which lets us estimate a measure of the normalized signal minus noise difference RS-N

Fig. 5 shows the setup for experiments. The DAVIS346
camera is connected to the host computer and the bias control runs on the host. We control the DVS event rate bychanging the speed and contrast of the displayed stimulus(here a recorded rotating dot).

5.2. Effect of bias tweaks
This section presents measurements of the effect of bias
tweaks on event rate and signal versus noise.
5.2.1

Effect of threshold on event rate

Estimating the event threshold from the bias currents allows
plotting the measured event rate versus estimated threshold. Fig. 6 compares the measured results with the theoryof Sec. 3.2 with a rotating dot and driving video. The dot
3 DVSBiasController.java in jAER on github

dotComputer
controlled
rotating
disk

SourceOutputDAVIS3436

Fig. 5: Setup for bias control experiments (except for
Fig. 7). The source monitor displays a stimulus whose activity rate and contrast can be easily controlled. The capture monitor displays the output of the Inivation DAVIS346
camera [18] and the bias controller and tweaks interface.

Event rate R (MHz)

3

0

DVS

60

Refractory period tweak Τrefr
-0.2
Motor
speed
setting

2

40

-0.4

1

-0.6

Event
rate R

0

0

100

200
time (s)300

20Motor speed setting (Hz)Control panel01ms
snapshots
2.5

Driving

1000

500

0

Event rate R (MHz)Total event rate (kHz)1500

Rotating dot
Higher constrast
Lower constrast

Σmin
3

4

5

6

7

Temporal constrast sensitivity Σ (event/e-fold)

1.5

26

1.0
0.5

−0.6

blank screen
noise

2

Motor speed setting (Hz) 54
50
43
36

2.0

8

Fig. 6: Measured total event rate versus estimated sensitivity(Σ = 1/θ) for rotating dot and driving scenes.
video is presented with two different contrasts. Over a sensitivity range of [2:5] event/e-fold there is a linear relationship; above this Σ range, noise increases the event rate. The
threshold sensitivity Σmin is about 2.2 for all of the tested
inputs. The gain factor depends on the visual input, but theintercept determined by the maximum contrast in the visual
input, since contrast smaller than the threshold does not trigger events.
5.2.2 Effect of refractory period on limiting event rate
Fig 7 shows limiting event rate with Trefr control. Since therefractory period affects the dead time after each event, weused a physical continuously rotating dot to produce continuous DVS input rather than the synchronous input produced by a computer monitor. The DC motor speed wascontrolled by PWM output from an Arduino Nano. We varied the speed of rotation under computer control and measured the output event rate R using various Trefr settings.
Decreasing Trefr decreased the event rate. The 1 ms snapshots show that increased refractory period erases repeated

−0.4

−0.2

Refractory period tweak Τrefr

0.0

Fig. 7: Measured event rate as a function of time and for
different motor speed and refractory period tweak Trefr settings.
events and the trailing ON edge of the black dot. The summary plot of event rate versus refractory period shows that
Trefr systematically affects the event rate and the effect isnearly linear.
5.2.3 Effect of bandwidth on signal and noise RS-N
Fig 8 shows experimental results illustrating the concept of
optimizing signal versus noise presented in Sec. 4.2. The
input in this experiment was 5 rotating dots. As we increase
the bandwidth tweak TBW , the signal event rate RS increases
but tends to saturate as it becomes high enough to capture
the movements of the dots. The noise event rate RN continues to increase steeply. The signal rate appears to increase
more steeply for the final TBW = 1.0 setting, but this increase is a byproduct of greatly increased noise, which is
misclassified as signal. A maximum RS-N occurs for tweak
values around zero. (The RS-N is still negative even at thispeak, because for this scene, the signal event rate was always less than the noise event rate.) Big changes in event
rate occur every time TBW is changed which makes the measurement quite difficult.

signalEventRate(Hz)500

RN Noise (kHz)400

noiseEventRate(Hz)

B

3

20

Scene: 5 rotating dots
0.2 Hz

15

300

RS

10

200
5

100

RN

0

RS Signal (kHz)A

2

RS-N=(S-N)/(S+N)

eak
h tw

Τ BW

0
Controldisabled

+1

0

25

50

75

Time (s)

2

125

100

Fig. 8: Measured signal and noise (both estimated from (9))
versus bandwidth tweak TBW . Input is 5 rotating dots displayed on flicker-free monitor. Note large transients causedby tweak changes.

RH (Hz)RL(Hz)

10000050000

Rotationstarted

10000

Rotation stopped

3

+1

2

0

1

Event Rate
State

0

R>RH

1

10

-1

Threshold
tweak Τθ

20

30
time (s)

0

R<RH

-1

Refractory tweak Τrefr

0

50

time (s)100150Fig. 10: Fixed-step event rate limiting using refractory period tweak Trefr and RH = 500kHz.

Event rate R(Hz)

1000000

5000

Rotationslowed-1-0.90500000

Event rate state

3

-0.80

-1.00

RH

0

dt

dwi

Ban

RS-N

-0.70

Event rate R (MHz)1

C -0.50-0.60

Fast rotation
starts40

5.3.2 Limiting high event rate with refractory period
Fig 10 shows the results of an experiment to control event
rate R below a maximum limit RH by using Trefr . We displayed input to the DVS where we could control the numberof generated DVS events by varying the speed of the input.
Initially, the control is disabled, and the R goes up to over
2 MHz. Then control is enabled. While R ≥ RH , Trefr is decreased. After 5s, R is controlled to be under RH . The cycle
is repeated several times.

50

Fig. 9: Fixed-step event rate bounding using threshold
tweak Tθ and [RL , RH ] = [100, 300] kHz.

5.3. Control experiments
This section presents results of control experiments.
5.3.1 Bounding event rate by controlling threshold
Fig. 9 shows the result of an experiment on bounding event
rate using fixed-step threshold control. The input was a single rotating dot for which we varied the speed of rotation(and occasionally stopped) to vary the event rate. We arbitrarily set RH = 300 kHz and RL = 100 kHz for this experiment; in practice they would be determined by system-levelconsiderations. We can see that the threshold tweak varies
according to the control policy to bring the event rate within
the [RL , RH ] bounds.

5.3.3 Regulating noise with bandwidth control
Fig 11 shows the results of an experiment to regulate noise
per pixel RN close to RNL by using TBW . In a stationary
scene, we turned the light illuminating the scene off andthen on again. Turning the light off increased the noise RN ,so the controller decreased TBW until RN went below RNL
by the required hysteresis factor of H = 1.5. Then weturned the light back on. This caused TBW to increase backto its original value. Thus the noise controller successfully
regulates the DVS pixel shot noise.

6. Discussion and conclusion
Table 2 compares the control method advantages anddisadvantages. Regulating event rate is clearly practical.
Either threshold or refractory period control can be used.
Threshold control has the advantage of increasing sensitivity for low contrast features, but increased sensitivity increases noise. Additionally, changes of threshold (partic-

Hz/pixel

on
Light
off
1 noise
RN
0

state

3
2
1

ok

tignore
RNL

HRNL
RNL/H

too noisy

0-0.5 bandwidth
tweak ΤBW
20
0

too quiet

40

60
time (s)80100Fig. 11: Noise regulation using bandwidth tweak TBW .
Control
Event
rate

Bias
Threshold

Event
rate

Refractory
periodNoise

Bandwidth

Advantages
Does notdiscard highfrequency
information.
Only minor
control artifacts.
Controlled
noise.

Disadvantages
Increased brightness quantization with higher threshold.
Decreasing threshold causes
event bursts.
Increased refractory period
discards high spatio-temporalfrequency information.
Large global event transient artifacts caused by TBW changes.
Difficult to measure RS-N overshort time scales.

Table 2: Comparing controllers.
ularly decreases) introduce significant transient bursts ofevents. A more sophisticated controller based on a more
precise model would allow sparser control action and sowould limit the undesired transient effects. It would also
speed up control, making it suitable for wider range of applications. A good first choice would be a proportional controller which we predict to perform well thanks to linear dependence between Tθ and event rate. Proportional control
may be particularly effective if one knows R0 (from (5))
and are able to infer Σmin online.
Changing refractory period does not cause transient control artifacts, but leads to loss of high spatio-temporal frequencies. For example, for our test case of a spinning black
dot, a long refractory period erases the trailing ON edge ofthe dot. The effect of a long refractory period on textured
objects would be to erase much of the detailed texture contained within the object, leaving only the leading edge andsome of the internal structure.
Automatic control of signal and noise tradeoff is moredifficult: Conceptually, a particular tradeoff of signal versus noise is arbitrary, since the relative cost of admitting
more noise or losing more high frequency signal depends
on the application. Practically, it is hard to measure a metriclike RS-N metric in a dynamically-changing scene. The measurements of Fig. 8 were obtained with a well-controlled,unvarying stimulus where signal events were designed to be

as constant as possible. In real scenarios, signals would usually change in an unpredictable way. Measuring RN to limit
it is more practical, but each bandwidth tweak change introduces a lot of transient artifact events, because the global
DC changes of photoreceptor output cause large bursts ofnoise events. Therefore, the controller must ignore the output until the transient disappears. Automatic noise regulation would be useful for long-term control, e.g., in surveillance and environmental monitoring, where the noise measurements would be reliable and bandwidth control actions
would be infrequent, e.g. every few minutes.
The results in this paper on automatic feedback control
of DVS camera pixel biases should be regarded as a starting point for future improvements. The controllers are currently designed to work in isolation, but could be combined,
e.g. noise could be controlled by bandwidth and threshold.
Bias control could incorporate time of day [10], lighting,and temperature information. Absolute lighting information
is available from sensors that include intensity output like
DAVIS but could also incorporate a simple light sensor ora global photocurrent measurement such as in [1]. Temperature is available from the camera’s inertial measurement
unit [19]. These sensor readings can be fused with DVS output statistics for quicker and more robust bias control.

Acknowledgements
This work was supported by Swiss SNF grant SCIDVS(200021 185069). We thank reviewers for useful comments.

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