31-07-2014, 11:24 AM
Traffic Lights Recognition in a Scene using a PDA
[attachment=66411]
Abstract
We present a project that consists in the Pedestrian Traffic Lights
Interpretation oriented for blind people. It is implemented on a mobile and
autonomous system, a PDA with a Video Camera incorporated. The traffic lights
interpretation is based on the analysis of the images taken by the Video Camera
and the general problem can be defined as finding a certain object in a complex
scene.
The complete system integrates other adapted functions for disabled people,
as for example a vocal agenda that features word recognition on an appointment.
The problem is subdivided in two different processes : the Segmentation
Process and the Recognition Process. In the first stage, the image is processed to
find the contours of the objects of interest using a colour segmentations. In the
second stage, we use structural methods over the patterns selected to decide if the
pedestrian traffic light is Green (moving figure) or Red (stopped figure).
The system should allow us at medium-term, the learning and recognition of
multiple images specific for the user (multiple objects) with the object of becoming
an adaptable technical help.
Introduction
The objective of this project is to
provide a solution for one of the problems
that visual handicapped persons find in an
urban environment : crossing a road
governed by a traffic light.
The utilisation of a PDA (Personal
assistant) is a good solution because it is a
portable, autonomous and generic system
with an assumable price.
Actually PDAs are potent enough to
grant a high computational cost, as this
project requires (Processor at 400 Mhz).
Their evolution is quick, and at mid-term we
will be able to find PDAs with similar
features at the actual PC
-Problem Presentation
The recognition problem is difficult
because of the complexity of the scene : the
variability of the position of the traffic light,
the different sizes of them depending on the
distance or the optic, the different shapes of
the silhouette, and the big variation in the
brightness of the colours (Fig.1, Fig.2). Also
other cases face us to interesting theorist (or
heuristic) problems : road crossing in two
times, or contradictory information (Fig.10).
A database with 200 images is freely
available on :
http://ufr6.univ-paris8.fr/desshandi/bdfeux
due to allow us to have other comparative
results
Elimination of the regions without interest
Night : In the night images (Fig.1),
using a colour threshold we can isolate the
objects that are very luminous. The value for
the threshold can be high, in-between 90% /
95% of the maximum intensity value, in the
plane R or G, depending on the silhouette
searched. This process allows us to isolate
the relevant forms.
Day : The colour threshold is less
efficient. It is completed with other
knowledge criteria that allow us to find the
traffic light in the scene, as the position of
the zebra crossing and the traffic post
(Fig.2).
Also in the two cases, if we observe the
scenes, it is not necessary to use the top and
the bottom part, because the traffic light will
not be there. We discard then a 35% of the
size of the image, 10% at the top and 25% at
the bottom
Recognition Stage
In this stage we receive the binary image
of the 4-connexe closed and discrete curve
contours.
The first approximation is using the
Freeman Code [4] to code the silhouette
models (example of the models Fig.4), that
are different depending on the size (height).
After this, we code all the figures found in
the segmented image.
With this strings, we use multiple
detection criteria :
The Edit Distance to find the most
similar string,
Determination of Axial Symmetry
(red silhouette),
Structure detection using structural
methods (grammar) to determine if
we have found a traffic light
silhouette.
Similarity Measure
The Similarity Measure in between
strings is calculated with the Edit Distance
[4]. The Edit Distance defines three basic
operations that will allow us too change one
string in the other one. We will associate a
cost for each operation:
Substitution : a → b cost γ (a,b)
Insertion : λ → a cost γ (λ,a)
Elimination : a → λ cost γ (a, λ)
The edit distance will the cost of the
basic operations made to convert one string
in the other. The additive of the criteria
assures we can use Dynamic Programming.
Also we can use another criteria, the
symmetry of the red silhouette. In the
(Fig.7) we can see the axial sy
Conclusion
The utilisation of no-specific equipment
to solve technical help for sensorial
handicapped is a good solution for their
integration.
The treated problem is complex and it
depends on different parameters with a big
variability (brightness, different forms,
distance, etc). The implementation results in
particular in the night case are really
encouraging.
The used technology is also a source
problem. The video camera used, does not
allow an optic zoom to take a better image
of the region of interest. So we find a
resolution problem in the recognition of the little traffic lights in the scene (Fig.9), but
this kind of obstacles will be solved with the
advance of the technology.
We have worked on the static images
taken from the PDA resolution. We have not
started the camera movement question, that
could bring us new complications.
[attachment=66411]
Abstract
We present a project that consists in the Pedestrian Traffic Lights
Interpretation oriented for blind people. It is implemented on a mobile and
autonomous system, a PDA with a Video Camera incorporated. The traffic lights
interpretation is based on the analysis of the images taken by the Video Camera
and the general problem can be defined as finding a certain object in a complex
scene.
The complete system integrates other adapted functions for disabled people,
as for example a vocal agenda that features word recognition on an appointment.
The problem is subdivided in two different processes : the Segmentation
Process and the Recognition Process. In the first stage, the image is processed to
find the contours of the objects of interest using a colour segmentations. In the
second stage, we use structural methods over the patterns selected to decide if the
pedestrian traffic light is Green (moving figure) or Red (stopped figure).
The system should allow us at medium-term, the learning and recognition of
multiple images specific for the user (multiple objects) with the object of becoming
an adaptable technical help.
Introduction
The objective of this project is to
provide a solution for one of the problems
that visual handicapped persons find in an
urban environment : crossing a road
governed by a traffic light.
The utilisation of a PDA (Personal
assistant) is a good solution because it is a
portable, autonomous and generic system
with an assumable price.
Actually PDAs are potent enough to
grant a high computational cost, as this
project requires (Processor at 400 Mhz).
Their evolution is quick, and at mid-term we
will be able to find PDAs with similar
features at the actual PC
-Problem Presentation
The recognition problem is difficult
because of the complexity of the scene : the
variability of the position of the traffic light,
the different sizes of them depending on the
distance or the optic, the different shapes of
the silhouette, and the big variation in the
brightness of the colours (Fig.1, Fig.2). Also
other cases face us to interesting theorist (or
heuristic) problems : road crossing in two
times, or contradictory information (Fig.10).
A database with 200 images is freely
available on :
http://ufr6.univ-paris8.fr/desshandi/bdfeux
due to allow us to have other comparative
results
Elimination of the regions without interest
Night : In the night images (Fig.1),
using a colour threshold we can isolate the
objects that are very luminous. The value for
the threshold can be high, in-between 90% /
95% of the maximum intensity value, in the
plane R or G, depending on the silhouette
searched. This process allows us to isolate
the relevant forms.
Day : The colour threshold is less
efficient. It is completed with other
knowledge criteria that allow us to find the
traffic light in the scene, as the position of
the zebra crossing and the traffic post
(Fig.2).
Also in the two cases, if we observe the
scenes, it is not necessary to use the top and
the bottom part, because the traffic light will
not be there. We discard then a 35% of the
size of the image, 10% at the top and 25% at
the bottom
Recognition Stage
In this stage we receive the binary image
of the 4-connexe closed and discrete curve
contours.
The first approximation is using the
Freeman Code [4] to code the silhouette
models (example of the models Fig.4), that
are different depending on the size (height).
After this, we code all the figures found in
the segmented image.
With this strings, we use multiple
detection criteria :
The Edit Distance to find the most
similar string,
Determination of Axial Symmetry
(red silhouette),
Structure detection using structural
methods (grammar) to determine if
we have found a traffic light
silhouette.
Similarity Measure
The Similarity Measure in between
strings is calculated with the Edit Distance
[4]. The Edit Distance defines three basic
operations that will allow us too change one
string in the other one. We will associate a
cost for each operation:
Substitution : a → b cost γ (a,b)
Insertion : λ → a cost γ (λ,a)
Elimination : a → λ cost γ (a, λ)
The edit distance will the cost of the
basic operations made to convert one string
in the other. The additive of the criteria
assures we can use Dynamic Programming.
Also we can use another criteria, the
symmetry of the red silhouette. In the
(Fig.7) we can see the axial sy
Conclusion
The utilisation of no-specific equipment
to solve technical help for sensorial
handicapped is a good solution for their
integration.
The treated problem is complex and it
depends on different parameters with a big
variability (brightness, different forms,
distance, etc). The implementation results in
particular in the night case are really
encouraging.
The used technology is also a source
problem. The video camera used, does not
allow an optic zoom to take a better image
of the region of interest. So we find a
resolution problem in the recognition of the little traffic lights in the scene (Fig.9), but
this kind of obstacles will be solved with the
advance of the technology.
We have worked on the static images
taken from the PDA resolution. We have not
started the camera movement question, that
could bring us new complications.