VISUAL-BASED DETECTION AND TRACKING OF DYNAMIC

OBSTACLES FROM A MOBILE ROBOT

Dora Luz Almanza-Ojeda, Michel Devy and Ariane Herbulot

CNRS, LAAS, 7 avenue du Colonel Roche, F-31077 Toulouse, France

Universit´e de Toulouse, UPS, INSA, INP, ISAE, LAAS-CNRS, F-31077 Toulouse, France

Keywords:

Moving obstacles, Detection, Tracking, Clustering, Monocular vision.

Abstract:

This paper presents a methodology for detecting and tracking moving objects during mobile robot navigation

in unknown environments using only visual information. An initial set of interest points is detected and then

tracked by the Kanade-Lucas tracker (KLT). Along few images, point positions and velocities are accumulated

and a spatio-temporal analysis, based on the a contrario theory, is performed for the clustering process of these

points. All dynamical sets of points found by the clustering are directly initialized and tracked as moving

objects using Kalman Filter. At each image, a probability map saves temporally the previous interesting point

positions with a certain probability value. New features will be added in the most likely zones based on this

probability map. The process detection-clustering-tracking is executed in an iterative way to guarantee the

detection of new moving objects or to incrementally enlarge already detected objects until their real limits.

Experimental results on real dynamic images acquired during robot outdoor and indoor navigation task are

presented. Furthermore, rigid and non rigid moving object tracking results are compared and discussed.

1 INTRODUCTION

A key function required for autonomous robot naviga-

tion, must cope with the detection of objects close to

the robot trajectory, and the estimation of their states.

This function has been studied by the robotic and the

ITS (Intelligent Transportation Systems) communi-

ties, from different sensory data such as laser, radar,

vision, among others. For driver assistance, many

contributions concern laser-based obstacle detection

and tracking (Vu and Aycard, 2009). In order to track

obstacles, it is required to estimate from the same sen-

sory data, the egomotion of the vehicle: a global prob-

abilistic framework known as SLAMMOT (Simulta-

neous Localization and Mapping with Mobile Object

Tracking) has been proposed by Wang et al. (Wang

et al., 2007). But in spite of numerous contributions,

this function still remains a challenge when it is based

only on vision. So this paper proposes a strategy to

detect static points, and moreover to detect and clus-

ter the moving ones in order to track mobile objects:

it is the ﬁrst step towards the full integration of a Vi-

sual SLAMMOT approach. It is proposed to reach

this objective, using only a monocamera system.

There are various methods for feature detection

and tracking using a single mobile camera. A. J. Davi-

son (Davison, 2003) has proposed a spatio-temporal

approach, in order to detect and track 2D points

from an image sequence and then to reconstruct cor-

responding 3D points used to locate the camera.

The Harris detector allows to initialize points to be

searched in successive images from an active strat-

egy, i.e. selecting the best points to be tracked. Each

point is represented by its appearance, i.e. a n × n

template around its ﬁrst position. The system state

at instant time t, is given by the 3D position and the

speed of every tracked point, as well as the position

and the speed of the camera. From this state, every

point position is predicted in the image acquired at

time t + 1, forming an elliptic zone where the point

must be found if the global state is consistent. Then,

each interest point is searched in its predicted zone by

a similarity measurement based on correlation score

using its template. A clever strategy must be used to

update the point template in order to better handle lu-

minance variations and partial object occlusions.

A method widely used for robotics applications

is based on the optical ﬂow. Dense optical ﬂow is a

hard computing procedure that makes it not so inter-

esting for real time applications. However, if optical

ﬂow is only extracted for interest features, i.e. for a

very small part of total image points, a tracking pro-

Luz Almanza-Ojeda D., Devy M. and Herbulot A. (2010).

VISUAL-BASED DETECTION AND TRACKING OF DYNAMIC OBSTACLES FROM A MOBILE ROBOT.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, pages 98-105

DOI: 10.5220/0002954800980105

 SciTePress

cedure of many objects characterized by some points,

could be applied in real time. Such a sparse solution

has been proposed by Shi-Tomasi (Shi and Tomasi,

1994) and it is commonly used in computer vision

because of its simplicity and its low computational

cost. Our own method is based also on this method

as a valid and conﬁrmed procedure, that can be ap-

plied in a real time context during navigation. Among

others, let us cite the work presented in (Lookingbill

et al., 2007) where authors have used the optical ﬂow

ﬁeld to leverage the difference in appearance between

objects at close range and the same objects at more

distant locations. This information allows them to in-

terpret monocular video streams during off-road au-

tonomous navigation and to propose an adaptive road

following in unstructured environments. This method

has been evaluated for the navigation of an intelligent

vehicle in a desertic terrain.

However, once interest points and optical ﬂow are

extracted and tracked from an image sequence, it is so

important to distinguish which of those tracked points

represent moving objects. Clustering techniques are

the ﬁrst basic solution to this question, but unfortu-

nately most of them require initial information about

the scene as the number of clusters to ﬁnd. The suc-

cess of these methods highly depends on these param-

eters. T. Veit et. al (Veit et al., 2007) coped with the

same issue for the analysis of short video sequences.

They validate a clustering algorithm based on the a

contrario method (Desolneux et al., 2008) which does

not need parameter tuning or initial scene information

for ﬁnding clusters of mobile features. This approach

has been also used in (Poon et al., 2009) to detect

moving objects in short sequences; additionally, au-

thors obtain 3D components of feature points to better

detect the correspondence between points and mov-

ing objects on which they have been extracted. This

work presents experimental results on real images, ac-

quired from ﬁxed cameras, so that essential issues of

autonomous navigation are not considered.

This paper proposes moving object detection and

tracking on a robot navigation context based on KLT

tracker and the a contrario clustering. Then, the re-

sulted clusters are initialized as moving objects and

tracked by a Kalman Filter. At each iteration, im-

age locations are ponderated by a bi-variate pdf func-

tion when a point is tracked. This value represents

the probability that the location contains a dynamical

point, being 0 the most interesting location where to

ﬁnd an interesting point.

Next section explains the proposed strategy. KLT

procedure used to detect and track interest points is

brieﬂy described in section 3. Section 4 describes the

main concepts of the a contrario theory. The global

detection-clustering-tracking approach and the use of

probability map in a long sequence of images are pre-

sented in section 5. Experimental results on real im-

ages are presented and discussed in section 6. Finally

last section concludes and explains future works.

2 OVERALL DETECT, CLUSTER

AND TRACK STRATEGY

In this work only visual information in grey-scale ac-

quired from a mobile robot is used for scene analysis.

General block diagram in Figure 1 describes the pro-

cedure carried out to detect and track multiple moving

objects.

Features

Selection

Tracking

Features

Clustering

Objects

Tracking

Probability

Map

Figure 1: Algorithm to detect and track multiple objects.

N initial feature points are selected in the input

image(t) using the Shi-Tomasi approach. Feature lo-

cations in next image are searched by the KLT tracker,

based on correlation and optimization processes. Let

us suppose ﬁrst that no object is tracked; so the pro-

cess loops on N

successive images executing only

two tasks, feature tracking and update of the prob-

ability map. We will call time of trail, this set of

processed images, i.e. the number of images

used to accumulate positions and apparent velocities

of tracked features. In order to select new features,

we seek into the probability map the most interest-

ing zones in the image for searching new points, i.e.

points which have the higher probability to be on mo-

bile objects. KLT process is executed continuously in

this way, while the robot navigates in order to provide

new visual information at each time of trail. Details

of the KLT theory are explained in next section.

At the end of each time of trail, only moving KLT

features are selected for being grouped by the a con-

trario clustering method. Here, moving KLT features

are characterized by a velocity vector higher than a

threshold set to 1 pixel. Resulted clusters represent

moving objects in the scene; every one is directly

VISUAL-BASED DETECTION AND TRACKING OF DYNAMIC OBSTACLES FROM A MOBILE ROBOT

initialized as a moving object and then tracked with

a position and an apparent velocity estimated by a

Kalman Filter. The Object tracking task exploits the

KLT tracker: it veriﬁes the consistency between the

point tracking in the current image and the Object

characteristics. At every iteration, this object could be

merged with another detected object based on similar

velocity and close position. Implementation details of

both clustering and merging process are presented in

section 4. Finally, object current positions are stored

in the probability map with the highest probability of

occupation, so this zone of the image is not interesting

to look for new features.

3 FEATURE SELECTION AND

TRACKING

Optical ﬂow procedure used in this work is based on

the initial technique proposed in (Lucas and Kanade,

1981) and on the well-known Select Good Features to

Track algorithm (Shi and Tomasi, 1994). This tech-

nique is largely used in the robotics community be-

cause it proposes to match the more salient points,

minimizing the processing time.

Detection of Moving Points. N distinctive feature

points are initially extracted from image t

by the

analysis of spatial image gradient in two orthogonal

directions. Locations of these N points in next image

are obtained by maximizing a correlation measure-

ment over a small window. Iterative process is accel-

erated by constructing a pyramid with scaled versions

of the input image. Furthermore, rotation, scaling and

shearing are applied on each correlation window by

optimizing a linear spatial transformation parameters

during iterative process. Once displacement vectors

are obtained for all initial features, apparent veloci-

ties are estimated based on displacement vectors.

This KLT optical ﬂow method is sparse because

only few points are initially selected to describe im-

age content. This sparse method is used in order to

save processing time, because some other essential

tasks must be performed. Figure 2 (frames 54 to 58

extracted from the sequence shown in second row of

Figure 5) depicts feature points detected and tracked

during a time of trail from N = 150 initial selected

points. From these accumulated locations, blue points

represent points with long optical ﬂow displacements.

For these points the assumption that they belong to a

moving object has a high probability.

Perception on Moving Objects. When moving ob-

jects are detected, the number of dynamic points is

subtracted from the N points tracked permanently by

KLT. Thus in following iterations, KLT will select

less than N new points. This strategy allows to track

only a ﬁx number of N points between KLT and the

Object tracking process. This rigorous control on the

number of points is important in our methodology be-

cause long image sequences will be evaluated and the

performance directly depends on the number of pro-

cessed points.

Figure 2: Accumulated Optical ﬂow in 5 successive images

from the sequence shown in the second row of Figure 5.

Figure 3 shows N feature points with locations

tracked by KLT process at three different times of trail

(8, 18 and 30). After 18 images and without any prior

knowledge of the environment, we perceive consis-

tent points motions, caused by the camera movement.

By analyzing optical ﬂow extracted from images ac-

quired from a moving camera in dynamic environ-

ments, we deduce that larger is the time of trail, better

will be the perception of moving objects, but also of

the camera motion. In order to minimize the egomo-

tion impact, we propose to reduce this time to 5 im-

ages which covers up egomotion and also, points out

independent movements. This can be veriﬁed in Fig-

ure 2; a constant movement in the cloud of points can

be seen in the middle of the image; egomotion is less

remarkable. Furthermore, the best advantage of this

choice N

= 5 is the reduction of the waiting time

before performing the a contrario clustering task.

4 A CONTRARIO METHOD AND

MERGING PROCESS

Visual perception is a complex function that requires

the presence of ”salient” or ”evident” patterns to iden-

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100

Figure 3: From N = 150 selected points in the ﬁrst image, tracking results on a sequence acquired when the robot turns left:

accumulated Optical ﬂow along 8 (left image), 18 (middle) and 30 (right) images.

tify something that ”breaks” the continuous motion

due to the camera egomotion. This ”salient pattern”

corresponds to ”meaningful event” detected by the a

contrario method (Cao et al., 2007). Basic concepts

of the a contrario clustering inspired by the Gestalt

theory, are exposed in (Desolneux et al., 2003) and

deeply in (Desolneux et al., 2008). In general, Gestalt

theory establishes that groups could be formed based

on one or several common characteristics of their el-

ements. In accord to this statement, an a contrario

clustering technique (proposed by Veit, et. al. (Veit

et al., 2007)) identiﬁes one group as meaningful if all

their elements show a different distribution than an

established background random model. Contrary to

most of clustering techniques, neither initial number

of clusters is required nor parameter has to be tuned.

In our context, i.e. unknown environment, approxi-

mated egomotion. .., these characteristics are very fa-

vorable.

4.1 Evaluation of a Background Model

We use the background model proposed in (Veit et al.,

2007) which establishes a random organization of the

observations. Hence, background model elements are

independent identically distributed (iid) and follow a

distribution p. The iid nature of random model com-

ponents propose an organization with not coherent

motion present.

Next, given an input vector V(x,y, v,θ) in R

, ﬁrst

objective is to evaluate which elements in V show a

particular distribution contrary to the established dis-

tribution p of the background model (that explains

”a contrario” name). To avoid element by element

evaluation, ﬁrst, a binary tree with V elements is con-

structed using a single linkage method. Each node

in the tree represents a candidate group G that will be

evaluate in a set of given regions designed by H . This

set of regions is formed by different size of hyper-

rectangles that will be used to test the distribution of

several data groups. Each region H is centered at each

element X in the group until ﬁnding the region H

that contains all the group and at the same time makes

minimal the probability of the background model dis-

tribution. Different size of hyper-rectangles are used

in function of data range, in our experiments we use

20 different sizes by dimension. Final measure of

meaningfulness (called Number of False Alarms NFA

in referenced work) is given by eq 1.

NFA(G) = N



min

X ∈ G,

H ∈ H ,

G ⊂ H

B(N − 1, n − 1, p(H

))

(1)

In this equation N represents the number of ele-

ments in vector V,



is the carnality of regions and

n is the elements in group test G. The term which ap-

pears in the minimum function is the accumulated bi-

nomial law, this represents the probability that at least

n points including X are inside the region test centered

in X (H

). Distribution p consists of four independent

distributions, one for each dimension data. Point posi-

tions and velocity orientation follow a uniform distri-

bution because object moving position and direction

are arbitrary. In other hand, velocity magnitude dis-

tribution is obtained directly of the empirically his-

togram of the observed data. So that, joint distribu-

tion p will be the product of this four distributions. A

group G is said to be meaningful if NFA(G) ≤ 1.

Furthermore two sibling meaningful groups in the

binary tree could belong to the same moving object,

then a second evaluation for all the meaningful groups

is calculated by Eq. 2.To obtain this new measure,

we reuse region group information (dimensions and

probability) and just a new region that contains both

test groups G

and G

is calculated. New terms are

′

= N − 2, number of elements in G

and G

, re-

spectively n

′

= n

− 1 and n

′

= n

− 1, and term T

which represents the accumulated trinomial law.

NFA

, G

) = N





′

, n

′

, n

′

, p



(2)

Both mesures 1 and 2 represent the signiﬁcance

of groups in binary tree. Final clusters are found

VISUAL-BASED DETECTION AND TRACKING OF DYNAMIC OBSTACLES FROM A MOBILE ROBOT

101

by exploring all the binary tree and comparing if it

is more signiﬁcant to have two moving objects G

and G

or to fusion it in a group G. Mathematically,

NFA(G) < NFA

, G

) where G

∪ G

⊂ G.

4.2 Merging Groups

This function is executed only if moving objects have

already been detected. O is a set of M objects that

will contain all candidate objects for merging evalu-

ation. That is, O = O

∪ O

where O

consists of

(1, 2, ..., k) moving objects tracked by Kalman ﬁlter,

and O

consist of (1, 2, ..., l) new moving clusters, in-

terpreted either as new moving objects, or part of ex-

isting ones . For each object in O, the velocity vector

is modeled by the mean of their velocity components

in X and Y, respectively represented by µ

and µ

We use these models to evaluate eq.3 that let establish

a decision constraint for merging.

min

i, j ∈ M,

i 6= j,

, O

⊂ O



s(µ

), µ

))

s(µ

), µ

))







(3)

We evaluate the similarity measure s which per-

forms the subtraction among velocity models for each

object in O. Parameters d

and d

are constant val-

ues set to one pixel in accord with the previous estab-

lished threshold for detecting moving features in KLT

process. This value is chosen in order to conserve the

best trade off between the threshold of moving points

in KLT module and the expected bias among object

velocities in the scene. This evaluation is carried out

in a linked way, where merged groups are removed

from O and added as a new object at the end of the list

with, obviously, a new corresponding velocity model.

This strategy allows the merging of the same objects

previously detected with a more enriched model. Fi-

nally, even when both O

and O

object velocities

are not resulted from the same process, they could be

compared because both are based on pixel displace-

ment in the scene (their optical ﬂow).

5 MOVING OBJECTS TRACKED

BY A KALMAN FILTER

Groups found by clustering technique are composed

of point locations at different processing time. Hence,

to conﬁrm that moving object is still in the scene, only

points present in last processed image are taken to ini-

tialize each cluster as a moving object in O

. There-

fore only the points located on the current image will

be used to model the moving object.

To track moving objects in O

along the image se-

quence, Kalman ﬁlter prediction evaluates a constant

velocity model. To initialize this model a vector state

is deﬁned for each moving object detected. Vector

state consists of the barycenter of object in X and Y

and its velocities are set to µ

and µ

values, respec-

tively. We assign the estimated position and velocity

calculated by Kalman ﬁlter to baricenter of the object.

This estimated position is used as the center of a win-

dow that will be extracted in the next image in order

to search the object points. We called this window the

zone of object and its size is a function of previousob-

ject limits and a security margin. Once object region

is extracted in next image, we carried out a correlation

process to ﬁnd new object location.

5.1 Model Object Detection

The concept of object developed here covers the man-

agement of several points which have been evalu-

ated as a part of a moving object. In this work, the

task of tracking consists in following each object el-

ement with its particular appearance and the only re-

lationship among them is their velocity and position.

Thus, model object initialization consists in extract-

ing a window patch (a template) around each point in

the image where object was detected. The same num-

ber of templates are extracted around the estimated

feature location in the next image. Appearance of ini-

tial templates in the current image is updated by an

afﬁne model. Feature points could be removed from

the model if one or more of the following cases hap-

pen:

• Feature location is not found by correlation

• Location found is not inside the bounding box

• Displacement and velocity of the points found are

not inside of the normally distribution of their re-

spective mean data.

5.2 Occupancy Map

In our algorithm, it is important to add new points

that complete the model of the detected moving ob-

ject and at the same time detect new incoming objects.

In a context of unknown environment, feature points

should be initially chosen in all the image without

highlighting some locations. To overcome this uni-

form point selection along robot navigation process,

a space-time occupancy map is constructed by cells

centered on tracked points indicating favorable loca-

tions for newfeature detection. This map develops the

idea of an occupation grid in the sense of higher prob-

abilities represent the locations in the image that are

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no important to seek for a new point (like occupied lo-

cations). So, mainly locations in the image with lower

probability values in the map will be ﬁrst used to look

for new points.

First probability map locations are initialized to

= 0.5 value that represents the initial fair selection

of feature points, that is, p(u, v, t = 0) = p

. When

an interesting point is detected in (u, v) at t > 0, it

becomes the center of an occupied cell and its cor-

responding value in the map is given by eq. 4 with

= 0. A cell becomes empty in function of interest

point displacements at every iteration : if a tracked

point leaves the cell it was using at time t −1 its prob-

ability is updated and then this cell is labeled as empty

in the current image t. The new cell in which tracked

point is located at time t becomes now an occupied

cell. The Figure 4 depicts simulated results of this

map construction along ﬁve successive images for a

tracked point. A square cell of size 11x11 centered

on (u, v) point location is used. Cell value is always

given by a 2D Gaussian pdf in combination with the

rules established by eq. 4 and 5. The inverse effect

of function α(u, v) applied to previous point positions

is clearly seen in this image. With this probability

map we enhance the assumption of new points of in-

coming object will appear behind its current detected

point positions.

p(u, v,t) =

∑

u,v∈Cell

(α(u, v)

+ G (µ

, µ

, σ

)) (4)

where α(u, v) function describes the previous

probability in the cell according to the previous state

of location (u, v), that is:

α(u, v)







− p(u, v)

t−1

if Cell

u,v,t−1

occupied

if Cell

u,v,t−1

empty

0 if (u, v, )

t−1

fair

(5)

where p

is the maximal probability value, set to 1

in our procedure. In order to avoid that this map stores

for long time the same probability values in some lo-

cations, the map is reset each 2 times of trail process,

except for the current object tracking locations.

6 EXPERIMENTAL RESULTS

AND DISCUSSIONS

Proposed algorithms have been implemented in C,

C++ and TCL and included as module into a frame-

work for developping algorithms in robotics. Robot

navigation was performed in indoor and outdoor con-

text with a camera mounted on the robot (640 × 480

100

120

180

200

220

240

260

280

300

0.2

0.4

0.6

0.8

180 200 220 240 260 280 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Figure 4: Simulated results of probability map for a tracked

point in 5 successive images. Inverse shapes represent the

probability in the four previous images, higher shape de-

picts current point location.

at 10Hz). The number N of tracked points by KLT

is set to 150. At this ﬁrst stage of the experiments,

any information about the odometry of the robot is

available, therefore, we carefully control robot speed

navigation.

First test was carried out on a sequence of 300 im-

ages. The ﬁrst row in Figure 5 shows three images

selected from this sequence, at the time of a mov-

ing object appears in the ﬁeld of vision of the robot.

Image 5a shows the bounding box of moving object

that enters from the left side of the image, all detected

points inside of rectangle are used to initialize object

model. After 20 processed images bounding box of

initial object is enlarged (see image 5b). Even when in

image 5c half of the moving object is out of the scene,

the object is still tracked by the Kalman Filter. This

is a specially difﬁcult sequence because some images

show drastic changes due to vibrations. So most of

the points enters to the clustering process, but their

movement is classiﬁed as random. The second row on

Figure 5 showsthe algorithm performance with object

occlusions. In this case the car which is closer to the

robot is well detected and tracked. Some problems to

perfectly detect the second car after the occlusion oc-

cur and image 5d shows that the algorithm divides it

in 3 different moving objects.

6.1 Non Rigid Moving Objects Tracking

An extension of tests to indoor environments is car-

ried out for evaluating the development of our algo-

rithm to track non rigid moving objects. Indoor envi-

ronment induces more stable illumination conditions

and normally a less charged background where non

rigid objects could be better handle. The Figure 6

VISUAL-BASED DETECTION AND TRACKING OF DYNAMIC OBSTACLES FROM A MOBILE ROBOT

103

(a) Image 91 (b) Image 111 (c) Image 131

(d) Image 58 (e) Image 66 (f) Image 86

Figure 5: Experimental results in outdoor environment. First row shows moving object detection and tracking: (a) initial

detection, then total detection in image (b) by merging function, ﬁnally tracking just the latest part of the car in (c). Second

row : moving object occlusion is successfully handle by our proposed method.

(a) Image 193 (b) Image 206 (c) Image 229

Figure 6: Indoor experimental results show the non-rigid objects detection and tracking. A good but incomplete detection is

done for a person who crosses the room image (a). Then a more enhanced detection is done in images (b) and (c) however

random natural turns during person walking perturb the successive detection of object points.

shows a person that crosses the ﬁeld of view of the

mobile robot during its navigation in an indoor con-

text. As long as the person moves away of the robot,

almost a complete detection is achieved. However,

with a considerable delay, initially only the head of

the person is detected. The leg movements are large

and in a forward-backward direction and it disturbs

the detection of a non random movement. To over-

come this restriction in our algorithm, a strategy that

involves the use of 3D camera and several point posi-

tions given by a SLAM process is being developed.

So, an additional but fast analysis comparison be-

tween the depths of moving points and detected mov-

ing object will provide enough information to asso-

ciate these points to moving object. The 2D dimen-

sional context of our proposed method is preserved by

transforming 3D points into 2D points before entering

to clustering process and at the end taking into ac-

count their third coordinate position only for achiev-

ing a faster and more complete non rigid detection.

7 CONCLUSIONS

Experimental results show that even with few images,

it is possible to detect a rigid (and partially non-rigid)

moving object by a spatio-temporal analysis of fea-

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104

tures. Object model is enlarged thanks to prior knowl-

edge managed by the proposed probability map. This

map is successfully used during the active search of

feature points because it mainly highlights zones that

certainly contain new moving interesting points. Our

tests are performed off line on a recorded sequence;

however, the global algorithm works fast and could

process images at 10Hz. The clustering method is the

highest time consuming in the global process; for that

reason, the number of trails to be grouped by the clus-

tering method, should be no more to 150 points. Thus,

the trade-off between image size and that number of

points guarantees the highest performance in overall

strategy.

It has been assumed that all pixels whose displace-

ments are less than one pixel could be considered as

noise or as points displaced by little vibrations of the

camera. However the most sensible part of our algo-

rithm resides in robot motion. Under not controlled

conditions of velocity, most signiﬁcant displacements

are concentrated in both left and right image sides,

mainly caused by egomotion. A general strategy to

avoid egomotion detection and non rigid moving ob-

jects is being integrated based on monocamera SLAM

approach. An interchange of 3D and 2D points infor-

mation between SLAM and our MOT process will be

continuously carried out giving a cooperative sense

to our new proposed strategy. That is, detected static

points will be sent to SLAM, these points are candi-

dates to be included as a new landmark in the stochas-

tic map used to update camera pose estimation. Then,

this camera pose will be received by our MOT pro-

cess to estimate the camera motion and calculate real

detected point displacements.

ACKNOWLEDGEMENTS

This work has been performed in the context of the

RINAVEC project funded by ANR, the french As-

sociation Nationale de la Recherche. It has been

supported by the scholarship 183739 of the Consejo

Nacional de Ciencia y Tecnolog´ıa (CONACYT), the

Secretar´ıa de Educaci´on P´ublica and by the mexican

government.

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