Optics and Photonics Journal, 2013, 3, 94-98
doi:10.4236/opj.2013.32B024 Published Online June 2013 (http://www.scirp.org/journal/opj)
A Neuro-inspired Adaptive Motion Detector
Xiaopin Zhong1, Lin Ma2
1College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
2Shaanxi international trust co.,ltd. Xian’an, China
Received 2013
ABSTRACT
In this paper, a novel motion detector is proposed to perceive the weak changes in a image sequence. This is inspired by
the mechanism of fixational eye movement and dynamics of vertebrate’s cortex. We realized respectively an artificial
model of visual attention selection, called dual-probe adaptive model (DPAM), and an active tremor operation (ATO)
approach. It is foun d that between them there exists a resonance phenomenon. The phenomenon is enhanced when the
ATO and the DPAM are in-phase and is suppressed when they are anti-phase. Based on this, we construct a novel mo-
tion detector combined by the ATO and the DPAM to resonate with the motion direction. This allows capturing moving
edges even in the image sequences with lighting change and noisy background. Simulation and Experimental results
demonstrate the effectiveness.
Keywords: Neuro-inspired; Motion Detector; Dual-pro be Adaptive Model; Active Tremor Operatio n
1. Introduction
Motion detection is an important basic process in many
video analysis tasks [1], such as object detection, behav-
ior recognition and video en coding. There exist a number
of typical approaches for motion detection. However,
they consider in an image sequence all of the areas in-
stead of the areas with motion.
The most used two well-known methods are the tem-
poral difference [2] and the background modeling [3].
They can work well only when the background is ap-
proximately stationary and the foreground is relatively
moving, i.e. they are sensitive to noise and variations in
illumination.
Marr-Ullman model [4] is an early motion detector
which realized a highly sensitiv e 1D directional-selective
detection by using the temporal derivative of zero-crossing
fragment of measurement. This detector passively re-
sponds to any significantly-moving object.
The optic flow [5] is also a successful visual motion
detection technique. There have recently emerged some
specific computing techniques for highly accurate optic
flows, but then they are still computationally inefficient.
In fact, human and primate’s visual system can not
only localize accurately moving objects, perceive their
moving direction and velocity. This offers the cognitive
ability to make use of the limited computing resource.
Bouzerdoum and Pinter [6,7] proposed a directional se-
lective multiplicative in hibitory motion de tector (MIMD)
under steady lighting condition. Based on MIMD, Wang
and Zheng [8] further developed a multiplicative inhibi-
tory velocity detector (MIVD) by replacing the low-pass
filter with a band-pass filter to determine the detectors
temporal feature. This replacement allows obtaining the
selectivi t y of motio n v ec t or for a mot i o n d etector.
Inspiring by the Sterlings model of the retinal nerve
circuits [9], a dynamic differential equation groups has
been set up by Ma et al. [10], which could be used to
capture the weak changing signals ex actly in noisy back-
ground with few parallel computing steps.
In the authors’ previous research [11], a group of dy-
namic differential equations has been set up to capture
exactly the weak changing signals in noisy backgrounds.
It is so-called the Dual-Probe Adaptive Model (DPAM).
Inspired by the mechanism of fixational eye move-
ments of human vision [13,14], we simulated an active
tremor operation (ATO). It is found that there is a reso-
nance phenomenon between the ATO and the DPAM.
This allows perceiving the motion direction. Based on
this finding, we propose th en an adaptiv e motion de tector.
This detector can perceive edges with specific moving
directions and adapt to changes of background and lighting.
The paper is structured as follows. In Section II, the
detailed adaptive motion detector is introduced and dis-
cussed. In Section III simulation and experimental results
are shown and analyzed and we conclude in Section IV.
2. Adaptive Motion Detector
2.1. Dual-Probe Adaptive Model (DPAM)
In fact, the so-call DPAM is a family of dynamic spatial

X. P. ZHONG ET AL. 95
temporal filters, and the parameter settings d etermine the
actual performance of a corresponding detector in re-
sponse to the input video. We denote first the model by
the equations as follows.
 ,
()( 1)(1)()
()=,( 1)
i
issishi s
ihs hihhi
s
kaskahk buk
hk akahk
 




sK
 (1)
,
()( 1),(( 1)())
iAiAi
ii
A
kk Akhk   AK (2)


()() ()
() ()
() ()
() ()
H
iii
DH
ii
HH
ib
i
DD
ib
i
bkskhk
bk bk
bk k
bk k









bK
bK
(3)
()()( ()())
()() (()())
DH
ii ii
H
ii ii
GkbkAkbk
GkbkAkb k






D
N
(4)
where , denotes the component
of the input intensity vector .
()
i
uk 1, ,ith i
()()k ui
s
k
h
and
stand for the component of the state variable feed-
forward vector and feedback vector respec-
tively. is the unit vector of coefficient of the
lateral inhibition effect of to
()
i
hk
th
i

ks
0

k
,hi K
i
h
j
s
in the surrounding
of pathway. This is to simulate a local grade po-
tential of nerve cells. denotes the gain coefficient
of photoreceptor. and
()
i
Gk 0
s
b
hs
a
s
h
a represent the transfer co-
efficient of ()
i
s
k and th e feedback co effici ent of (1)hk
i

to (
i)
s
khs
a
1
hs

. and are the coefficient of re-
newal equation and confined to the convex combination
.
0hh a0
hh
aa
The formula 2 is an adaptive threshold equation, where
()
i
A
k is the component of vector of , denotes
the adaptive threshold;
th
i()kA
0
A


,Ai ,Ai
K is the coefficient of re- newal equation; and () is the unit vectors
of coefficient of the lateral inhibition effect of
K0()
i
A
k to
()
j
A
k in the surrounding of pathway, which is
similar to and wider than it.
()
i
Gk
,hi
K
For convenience, we define in formula 3 four interme-
diate variables , ,
()
H
i
bk ()
D
i
bk ()
H
i
bk and ()
D
i
bk to
build respectively the corresponding vectors ,
, and b. They are also known as
bipolar varia bles named after bi p ola r cell of ret i na.
()
Hkb
()
Dkb( Hkb) () Dk
The formula 4 is exactly the dual push-pull probes
output equation of the model, where and
are the output dual probes of the model. The readers are
referred to [11] for the analysis and discussion of the
model parameters.
()k

G()k

G
2.2. Active Tremor Operation Based Detector
In the neurobiological research, three types of uncon-
scious eye movements are regarded during gazing a tar-
get. They are high frequency tremor, microsaccades and
slow drifts [12,16]. Further research illustrates that the
role of microsaccades and drifts is to latch down objects
and to compensate the noisy control of muscle [12]. Only
the tremor is believed to be related to visual perception,
i.e. visual fading on retina is inhibited by tremors [15].
Inspired by this point, we introduce an active tremor
operation (ATO) into the DPAM model [10,11]. Ac-
cording to the analysis in reference [11 ], when the update
coefficient is close to zero, the DPAM is reduced to a
typical image change detector. It is called DPAM-m de-
tector because the outpu t is like an M type cell of human
retina [18]. With this additional ATO, the selective-in-
hibitory visual fading is then realized in a DPAM-m de-
tector. We call this the ATO-DPAM-m detector in the
followings. See Figure 1 for the processing structure.
For convenience, we redefine the ATO and introduce
the basic process.
Definition 1: Active Tremor Operation (ATO) is a pe-
riodic translational operation of global image on digital
video flows. This operation repeats a same translation of
all pixels of a frame with an identical direction, ampli-
tude and frequency.
As shown in Figure 2, for video flow constructed
by image sequence ()uk
()
I
k, suppose that the translation
amplitud e and t h e fr equency o f A TO are and 0.25
round/s respectively, i.e. (dk)
()
() (),
ppdk
uk uk


 (5)
where denotes the coordinates of a pixel in image p
I
,
and (dk)

is a function of time k.
In the case of ATO-DPAM-m detector, the push-pull
outputs denoted by and mean the On-
type response and the Off-type response respectively.
()k


G()k


G
We found that there exists a resonan ce in the response
of an ATO-DPAM-m detector. This is because in the
DPAM-m detector, a visual fading occurs for the part
without change in the image sequence and an enhance-
ment response occurs for the part with change. In other
words, ATO enhances DPAM if they are in-phase and
ATO DPAM-m
()k

G

()k

G

()uk

()uk
Figure 1. Structure of ATO-DPAM-m detector.
Figure 2. An example of ATO process.

X. P. ZHONG ET AL.
96
ATO inhibits DPAM if they are anti-phase. Therefore,
the output of ATO-DPAM-m detectors has the selective
ability and is highly related to the directio n and the phase
of ATO. This provides a solid foundation of motion de-
tectors with directional selectivity.
To differentiate the directions, we use subscript
for the responds, i.e. and ,
where P means the positive operational direction and N
for the negative one. As shown in Figure 3 for example,
it is shown in a synthesized image sequence
, 
()(,PN)()k



Gk



G
()
p
pkv
uk I



that there occurs in-phase enhancement or anti-phase
inhibition according to the relationship between d

and velocity v, and the ATO phases.
2.3. Adaptive Motion Detector (AMD)
We consider further a pair of ATO-DPAM-m detectors
with inverse directions, i.e. a positive d irectiona l detector
called ATOP-DPAM- m and a negative one called
ATON-DPAM-m. See in Figure 4 for the configuration.
The parameters of both detectors are all identical except
that the difference of their phase is

and particularly
is set to 0.01. We then obtain a new ATO-based
adaptive motion detector.
hs
a
(a) (b) (c) (d)
(e) (f) (g) (h)
Figure 3. The environmental adaptive threshold and the
response of left tremor of a horizontal ATO-DPAM-m de-
tector with different phases and amplitudes for video flow
p
pkv
uk I() 

 formed by a left moving image I
According to the property of in-phase enhancement and
anti-phase inhibition, we compare the different combina-
tions of output response and , yielding a
new set of outputs, i.e. ()k



G()k



G
PPNN
PPNN
NNPP
NNPP
() () () ()
() () () ()
()() () ()
()() () ()
kkkk
kkkk
k kkk
k kkk











 
 


GGGG
GGGG
GGGG
GGGG
(6)
where and denote the positive On-type
output and Off-type output respectively. They selectively
respond to On-type and Off-type moving edges whose
moving directions are not i dent i cal to the posi ti ve di recti on
of the ATO. and represent the negative
On-type and Off-type output respectively. They selectively
respond to On-type and Off-type moving edges whose
moving directions are not identical to the negative direc-
tion of the ATO. As shown in Figure 5, the different
outputs of the proposed AMD can selectively respond to
the edges with specific motion characteristics.
P()k

G
G
P()k

G
)G
N(k
N()k 
In summary, the proposed AMD has realized a com-
binational selective detector to capture moving edges
based on the ATO resonan ce enhancement and inhibition
of edges in different moving directions. The method is
robust to backgrounds with change or switch due to the
environmental adaptive thresho ld.
3. Experimental Results and Analysis
In this section, we experiment using the KTH dataset [17]
and analyze the performance of the proposed adaptive
P()k

G

N()k

G

P
()k

G

N
()k

G

P
()k

G
N
()k

G
P
()k

G
N
()k

G
()uk
P
()uk

N
()uk


with ve-
locity v. (a) a stripe image example in the video flow; (b) the
environmental adaptive threshold; (c) and (d) demonstrate
the ATO-R response yielded by output for in-phase
edges and that yiel ded by output for antiphase edges
respectively when the ATO phase is
kG()



)kG(



2

; (e) and (f) show
the ATO-R response yielded by output for in-phase
edges and that yiel ded by output for antiphase edges
respectively when the ATO amplitude and the phase
is
k)
d
G(



kG()



v
32

. (g) and (h) show the visual fading on the output of
and
respectively when the ATO amplitude
and the ATO phase is
kG()


dv
kG()



32

.
Figure 4. The diagram of ATO-AMD’s configuration and
its output connection.
(a) (b) (c) (d)
Figure 5. The AMD response to a synthesized video input.
(a) is the synthesized video input; (b) shows the adaptive
threshold ; (c) and (d) shows respectively the positive
and negative direction responses, where the red regions
stand for On-type and the blue regions for Off-type.
Ak()

X. P. ZHONG ET AL. 97
motion detector (AMD). A number of behavior analysis
results have been reported based on the KTH dataset.
However their analysis involves only motion perception
and motion feature ex traction.
We construct a four-directional motion detector com-
bination which is formed by a pair of horizontal AMDs
and a pair of vertical AMDs to detect motion in KTH
videos with the movements of jogging, arm lift, and arm
down. See in Figures 6, 7 and 8 for detection results: (a)
a frame of input video; (b) the environmental adaptive
threshold A(k); (c) and (f) show the vertical edge re-
sponses, upward and downward respectively; (d) and (e)
illustrate the horizontal edge responses, towards the left
and the right respectively. What’s more, the red regions
denote On-type moving edges and the blue regions de-
note Off-t ype movi ng e d ges.
(a) (b) (c)
(d) (e) (f)
Figure 6. Detecting results of ATO-AMD on a video with a
body moving to the right.
(a) (b) (c)
(d) (e) (f)
Figure 7. Detecting results of ATO-AMD on a video with
arm lift movement.
(a) (b) (c)
(d) (e) (f)
Figure 8. Detecting results of ATO-AMD on a video with
arm dropped dow n.
From the results, we find this four-directional detector
combination can detect effectively both On-type and Of f-
type motion with different moving directions. For the
movements of jogging towards the right, the detector
barely responds to the movements towards the left, e.g.
Figure 6(d), while the detector for movements towards
the right br ings obv ious output respon se, e.g . Figure 6(e).
On the other hand, there occurs a little upward and down-
ward movement simultaneously with left-right movement.
Therefore the detectors for upward and downward mo-
tion can respond a bit to left-right movement. Likewise,
the other detectors generate similar results shown in Fig-
ures 6, 7 and 8.
4. Conclusions
Inspired by the neuroscience research, in this paper we
proposed a so-called dual-probe adaptive model (DPAM)
and an active tremor operation (ATO) approach to simu-
late the visual attention selection process and the fixa-
tional eye movement. We found that they enhance each
other if in-phase and inhibit each other if anti-phase.
Based on this finding, we has constructed a framework
by combining DPAMs and ATOs in two opposite direc-
tions. This framework can agilely capture the edges with
different moving directions even in the image sequences
with lighting change and noisy background. Therefore,
the proposed method provide an important basis for the
further study on motion analysis system.
5. Acknowledgements
This work was supported by the grants from Natural
Science Foundation of Shenzhen University (grant no.
201206). The authors would also like to thank the anony-
mous reviewers.

X. P. ZHONG ET AL.
98
REFERENCES
[1] C. W. G. Clifford and M. R. Ibbotson, “Fundamental
mechanisms of visual motion detection: models, cells and
functions,” Progress in Neurobiology, Vol. 68, No. 6,
2002, pp. 409-437.
doi:10.1016/S0301-0082(02)00154-5
[2] A. J. Lipton, H. Fujiyoshi and R. S. Patil, “Moving Target
Classification and Tracking from Real-time Video,” in
IEEE Workshop on Applications of Computer Vision,
1998, pp. 8-14.
[3] Y. Ren, C.-S. Chua and Y.-K. Ho, “Motion detection with
nonstationary background,” Machine Vision and Applica-
tions, Vol. 13, No. 5, 2003, pp. 332-343.
doi:10.1007/s00138-002-0091-0
[4] D. Marr and S. Ullman, “Directional Selectivity and Its
Use in Early Visual Processing,” Proceedings of the
Royal Society of London. Series B, Vol. 211, 1981, pp.
151-180. doi:10.1098/rspb.1981.0001
[5] B. K. P. Horn and B. G. Schunck, “Determining Optical
Flow,” Artificial Intelligence, Vol. 17, 1981, pp. 185-203.
doi:10.1016/0004-3702(81)90024-2
[6] A. Bouzerdoum and R. B. Pinter, “A Shunting Inhibitory
Motion Detector that Can Account for the Functional
Characteristics of Fly Motion-Sensitive Interneurons,” in
1990 IJCNN International Joint Conference on Neural
Networks, Vol.1, 1990, pp. 149-153.
doi:10.1109/IJCNN.1990 .137560
[7] A. Bouzerdoum, “The Elementary Movement Detection
Mechanism in Insect Vision,” Philosophical Transactions:
Biological Sciences, Vol. 339, No. 1290, 1993, pp.
375-384.doi:10.1098/r stb.1993.0037
[8] A. Wang, N. Zheng, L. Yuan et al., “Multiplicative In-
hibitory Velocity Detector (MIVD) and Multi-Velocity
Motion Detection Neural Network Model,” Proceedings
of International Conference on Multi-Sensor Fusion and
Integration for Intelligent Systems, 1996, pp. 476-483.
[9] P. Sterling, “Microcircuitry of the Cat Retina,” Annual
Review of Neuroscience, Vol. 6, No. 1, 1983, pp.
149C185.
[10] L. Ma, N.-N. Zheng and Z.-J. Yuan, “A Dynamic Model
for Early Vision Processing,” in IEEE International Con-
ference on Computer and Electrical Engineering, Phuket
Island, Thailand, 2008, pp. 96-100.
[11] L. Ma, N.-N. Zheng, Z.-J. Yuan et al., “A Novel Dual
Probes Adaptive Model for Image Change Detection,”
IEEE Signal Proceeding Letters, Vol. 17, No. 10, 2010,
pp. 863-866.
doi:10.1109/LSP.2010.2063023
[12] S. Martinez-Conde, S. L. Macknik, and D. H. Hubel,
“The Role of Fixational Eye Movements in Visual Per-
ception,” Nature Reviews Neuroscience, Vol. 5, No. 3,
2004, pp. 229-240. doi:10.1038/nrn1348
[13] I. Murakami, “Fixational Eye Movements and Motion
Perception,” in Twenty-eighth European Conference on
Visual Perception, A Coruna, Spain, 2005, pp. 35-35.
[14] S. Moshel, A. Z. Zivotofsky, L. Jin-Rong et al., “Persis-
tence and Phase Synchronisation Properties of Fixational
Eye Movements,” European Physical Journal-Special
Topics, Vol. 161, Jul, 2008, pp. 207-223.
doi:10.1140/epjst/e2008-00762-3
[15] M. Greschner, M. Bongard, P. Rujan et al., “Retinal Gan-
glion Cell Synchronization by Fixational Eye Movements
Improves Feature Estimation,” Nature Neuro sci enc e, Vol.
5, No. 4, 2002, pp. 341-347. doi:10.1038/nn821
[16] Engbert, Ralf. “Computational Modeling of Collicular
Integration of Perceptual Responses and Attention in Mi-
crosaccades,” The Journal of Neuroscience, Vol. 32, No.
23, 2012, pp. 8035-8039.
doi:10.1523/JNEUROSCI.08 08-12.2012
[17] Z. Gao, M. Chen, et al. “Comparing Evaluation Protocols
on the KTH Dataset,” Human Behavior Understanding
(2010): 88-100.
[18] W. Hui, Q. Zuo and X. Guan, “Main Retina Information
Processing Pathways Modeling,” International Journal of
Cognitive Informatics and Natural Intelligence, Vol. 5,
No. 3, 2011, pp. 30-46.
doi:10.4018/IJCINI.20110701 02