Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile
Mapping Images
Steven Puttemans, Stef Van Wolputte and Toon Goedem
EAVISE Research Group, KU Leuven, Jan Pieter De Nayerlaan 5, Sint-Katelijne-Waver, Belgium
Cycloramic Imagery, Mobile Mapping, Pedestrian Detection, Application Specific Constraints, Soft Blurring.
When capturing images in the wild containing pedestrians, privacy issues remain a major concern for indus-
trial applications. Our application, collecting cycloramic mobile mapping data in crowded environments, is an
example of this. If the data is processed and accessed by third parties, privacy of pedestrians must be ensured.
This is where pedestrian detectors come into play, used to detect individuals and privacy mask them through
blurring. The problem of undesired false positive detections, typical for pedestrian detectors and unavoidable,
still leaves undesired areas of the images being blurred. We tackled this problem using application-specific
scene constraints, modelled by a height-position mapping based on scene-specific pedestrian annotation data,
combined with reducing the field of interest and case-specific false positive elimination classifiers. We ap-
plied a soft blurring technique to avoid the artificial look of simply applying Gaussian blurring to the found
detections, which results in an effective fully-automated masking pipeline for privacy safeguarding in mobile
mapping images. We prove that we can use pre-trained pedestrian detection models, but by collecting a limited
amount of application-specific annotations and by exploiting scene-specific constraints, we are able to boost
the detection accuracy enormously.
In mobile mapping applications, a vehicle equipped
with cameras is used to grab images in order to give
the user a digital view of the surroundings. This is
repeated at preset intervals in order to ensure that the
complete surroundings of the car are being captured.
Companies like Google, but also local land surveying
offices, are carrying out such measuring campaigns
to make digital images of streets across the globe.
When collecting all this data, one can imagine that
the amount of data increases drastically once someone
is capturing larger projects, e.g. the ‘Google Street
View’ application. The goal of capturing all this data
is providing users with fast, accurate and detailed data
measurements for producing all kinds of 2D and 3D
geographical information systems.
Avoiding pedestrians walking around when cap-
turing mobile data is nearly impossible, which raises
the question of privacy issues when they are. Espe-
cially when this data is shared with or sold to indus-
trial partners, it is important that the privacy of these
pedestrians is guaranteed. Therefore companies are
continuously looking for robust solutions able to filter
out privacy-sensitive content from the captured data.
One solution could be to manually browse the
data, indicating every pedestrian and making them
privacy-safe by applying a blurring filter to the an-
notations. In the case that the amount of data is rather
limited, this might be the fastest and most accurate
solution. However when the data size rises over sev-
eral millions of captured images a week, one immedi-
ately notices that this approach is no longer suitable.
In those cases an automated unsupervised approach is
preferred. One of the most frequently used techniques
in tackling this problem is applying pedestrian detec-
tion algorithms like (Dalal and Triggs, 2005; Viola
and Jones, 2001; Doll
ar et al., 2009; Felzenszwalb
et al., 2008) on the captured mobile mapping data,
marking possible pedestrian-like areas in the image.
These in turn can then be blurred or cut out, to avoid
transferring privacy-sensitive data.
A major downside of existing pedestrian detectors
is that they require the manual selection of a thresh-
old on the detection certainty score to find a good bal-
ance between finding actual pedestrians in the image
and avoiding false positive detections. If the thresh-
old is set too strict, we will only detect pedestrians
but be unable to find all of them, and thus privacy is-
sues arise again. If we put the threshold too sloppy, all
Puttemans, S., Wolputte, S. and Goedemé, T.
Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images.
DOI: 10.5220/0005784304060415
In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP, pages 408-417
ISBN: 978-989-758-175-5
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
pedestrians will be found, but similar objects or areas
will trigger a false positive detection such that other
objects will be blurred. The mobile mapping commu-
nity wants to avoid this at all costs, because most data
is used to derive GIS systems, which need to be as
accurate and complete as possible.
In this paper, we propose an effective post-
filtering step using scene-specific constraints, by set-
ting a sloppy detection certainty threshold, avoiding
false negative detections (missed pedestrians), but ad-
ditionally ensuring the removal of false positive de-
tections using several effective post-processing steps.
Furthermore we expand the system with additional
small color based classifiers able to remove even
more false positives. Finally we provide an elegant
soft blurring approach for safeguarding the privacy of
pedestrians inside the mobile mapping images.
The remainder of this paper is structured as fol-
lows. Section 2 presents related research, while sec-
tion 3 discusses the data collection. This is followed
by section 4 in which the proposed approach is dis-
cussed in detail. Finally section 5 elaborates on the
obtained results while section 6 sums up conclusions
and possible future improvements.
Pedestrian detectors come in different types and fla-
vors. The main difference lies in the flexibility of the
model, where we distinct between rigid and non-rigid
approaches. Rigid approaches focus on an object al-
ways being in the same constellation, with only one
large part trained as a model. Such approach is sug-
gested by (Dalal and Triggs, 2005; Viola and Jones,
2001), where a rigid model based on gradients is fed
to a support vector machine or a boosting step. A
downside is that they are trained on a fixed frontal
view of the object. Non-rigid approaches on the other
hand try to model objects as a combination of de-
formable parts, existing of several rigid parts (arms,
head, torso and legs), and a deformation relationship
between them (Felzenszwalb et al., 2008). As pedes-
trians tend to move and change position frequently,
we decided to use a non-rigid detector. Most pedes-
trian detectors discard color information, because of
the wide variation in clothes and appearance. How-
ever more recent techniques like (Doll
ar et al., 2009;
ar et al., 2010) show that including color informa-
tion can have a significant increase in performance.
(Van Beeck et al., 2012) introduces a warping
window approach where fast real-time vision-based
pedestrian detection is obtained by calibrating the
height and orientation of the pedestrian at each spe-
cific image location. We prove that we can apply a
similar technique, as a post-processing step after the
detection phase, by learning a relation between the
height and position of an average pedestrian from a
limited set of application-specific annotations.
(Puttemans and Goedem
e, 2013) proves that us-
ing application-specific information, is one way to
improve the accuracy of object detection algorithms.
Similar rules apply for pedestrian detection, as far as
the application allows you to find some application-
specific constraints. In our application we exploit the
fact that the camera is mounted on top of car, at a fixed
position with respect to the ground plane, resulting in
a relation between the position and the height of any
given pedestrian. Furthermore we exploit the anno-
tated training data to learn regions of interest, avoid-
ing the processing of undesired image regions, like
the sky or on top of buildings. (Cho et al., 2012; Peng
et al., 2015; Dibra et al., 2015) describes a similar use
of a ground plane assumption for 3D modeling and
multiple camera view processing.
For privacy masking, several solutions have been
proposed. (Tanaka et al., 2015) tries to define how
much blurring is needed to reach a certain level of pri-
vacy. (Panagiotis, 2015) applies a simple block based
blurring, whereas (Nakashima et al., 2015) suggests
to use image melding, replacing a person’s face with
a fixed neutral expression instead of blurring. Our
application still demands masking, but to avoid the
hardness of block based blurring, we propose to use a
smooth soft blurring approach.
This research is developed on top of two mobile map-
ping datasets, which are made publicly available
, to
encourage further research in this area.
The first dataset is a series of mobile mapping cy-
cloramic images with a resolution of 4800×2400 pix-
els, captured using a LadyBug 1 camera setup, in a
quiet and calm urban area in the Netherlands. The
captured images give a full 360 degree view from the
surroundings of the car at any given position. The
camera itself is fixed and mounted on the top of the
roof of the car. The set has 450 images under day-
light conditions. The dataset is used to develop and
fine-tune the suggested approach.
The second dataset was captured using a LadyBug
2 camera, having a resolution of 8000 × 4000 pixels,
again mounted on top of the roof of the car, containing
45 images of a train and bus station in Belgium. We
Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images
Figure 1: Example frames for both datasets used: (top)
Dataset 1 - urban area in the Netherlands; (bottom) Dataset
2 - Belgian train and bus station.
used this dataset to prove that the developed approach
is independent of the application-specific settings like
camera setup and application environment, except for
defining the actual height-position relation used to im-
prove the detection success rate. An example of both
mobile mapping datasets can be seen in Figure 1.
All database images were manually annotated to
provide ground truth data for the actual locations of
pedestrians. For the first dataset this led to 240 pedes-
trian annotations while the second dataset contained
1630 pedestrian annotations. The large difference is
mainly due to the recorded surroundings, where a
train and bus station is likely to have more pedestrians
walking around in each mobile mapping image.
Our approach can be split into several processing
blocks, as seen in Figure 2. First we create a limited
amount of ground truth annotations for both datasets,
needed for both building the height-position relation
and inferring the color-specific constraints for learn-
ing the false positive elimination classifiers. The an-
notations are also used for validating each additional
post-processing step. At runtime, we apply a multi-
scale pedestrian detection algorithm on the input data
provided in a sliding window manner, image, stor-
ing the detection results and their detection certainty
score. Based on the annotations we apply a valid re-
gion reduction, a height-position location relation and
a certainty score thresholding, all leading to an ef-
Figure 2: Block diagram of the suggested approach.
ficient pruning of the obtained detections. For spe-
cific object classes that still trigger false positive de-
tections, we design specific color based false positive
elimination classifiers, which in turn further improve
the results. The main goal is thus to remove as much
false positives as possible and increase the resulting
detection accuracy. The detections found are then
passed on to an elegant soft blurring step to ensure
privacy safeguarding.
For our research we used an implementation
(De Smedt et al., 2012) of the cascaded Felzenszwalb
latentSVM4 implementation, which uses a part based
object detector for efficient pedestrian detection. The
reason for this is quite straightforward. A part based
detector is non-rigid and thus captures the different
poses of pedestrians efficiently. On the other hand
we also have a fast and optimized C++ implementa-
tion available. However, our post-processing is inde-
pendent of the pedestrian detector used, so basically
it could be replaced by any out-of-the-box pedestrian
detector. This is one of the major benefits of our sys-
tem, encouraging cross-dataset evaluation.
A downside to every pedestrian detection algo-
rithm is that one must filter the output based on the
object detection certainty score, by selecting a spe-
cific threshold and thus locking down on a specific
point on the precision recall curve of that detector. If
we decide to put the threshold too sloppy, we get an
increase in false positive detections, while in the same
time, reducing the amount of false negative detections
(every single pedestrian will likely be returned). This
would lead to an enormous amount of privacy mask-
ing, however also removing a lot of useful informa-
tion from the image. This is unacceptable for the
mobile mapping community, where the data is used
to create high quality 2D and 3D GIS systems. On
the other hand, if we put the threshold very strict,
the amount of false positives will decrease drastically,
but we will get an increase in false negative detec-
VISAPP 2016 - International Conference on Computer Vision Theory and Applications
tions, which in our application of privacy masking
would not be acceptable. Our approach therefore uses
a sloppy threshold for obtaining every possible pedes-
trian as a true positive detection, then subsequently
using smart post-processing to efficiently remove as
much false positive detections as possible.
4.1 Scale-space Location Relation
When considering our application of mobile mapping
using a fixed 360 degree cycloramic camera, we know
that the actual height position of the camera, com-
pared to the environment, will be fixed, only if we
assume a flat ground plane, and if that ground plane
will never change drastically. This is a crucial scene
constraint, allowing us to take into account that every
pedestrian in the image, walking on the street or on
the sidewalks, will have an average fixed height in re-
lation to the position in the final cycloramic images.
People closer to the car and thus to the camera will be
larger, while people further away will move towards
the camera’s vantage points and thus be smaller. For
any given horizontal line in the image, we can state
that all pedestrians on that line will have the same av-
erage height, of course keeping in mind that we have
a natural height variance within pedestrians.
4.1.1 Mapping Ground Truth Annotations
In order to model a height-position relation we started
by mapping out the ground truth annotations collected
on the first dataset. The effort of annotating a smaller
part of application-specific data, to be used for deriv-
ing scene constraints, is small compared to training a
complete new pedestrian detector (which needs much
more annotations and processing time). The result of
these manual annotations can be seen in Figure 4(a),
where the height of each annotation is mapped in re-
lation to the position, defined as the center of gravity.
During the annotation phase only pedestrians on side-
walks, parks and roads were annotated. If a pedes-
trian would be standing on a balcony of a building,
this pedestrian was not taken into account.
Figure 3: Applying borders for minimal and maximal
pedestrian height, as defined by the blue borders in 4(a).
4.1.2 Model Fitting and Region Reduction
In relation to the data mapping seen in Figure 4(a) we
fit a linear model to the data points and apply a search
for image region boundaries. The red curve is the
fitted linear relation to the mapped annotation data,
which models the relation between pedestrian height
and pedestrian position, relative to the camera posi-
tion. The green borders are based on the assumption
that we have a Gaussian data distribution compared to
the fitted model, and that these borders should capture
99.8% of all detections using the rule of [3σ, +3σ].
The reasons for this allowed model deviation are quite
straightforward. First of all we have a natural devia-
tion in pedestrian height, while secondly, due to the
cars suspension, the camera height is not completely
fixed to the ground plane. Thirdly, there is a possi-
ble deviation from the flat ground plane assumption
caused by height differences due to sidewalks, defects
in the road, speed bumps, etc. The blue borders define
allowed position regions for pedestrians in the image,
assuming the training data covers a wide variance of
available pedestrian specific to the application. This
is visualized in Figure 3 and allows us to immediately
ignore detections that are outside these regions, re-
moving about 50% of the image, and thus lowering
the chance of false positive detections occurring.
4.1.3 Applying Constraints on Detection Data
Figure 4(b) visualizes the detections obtained by our
pedestrian detection algorithm. When applying the
realistic pedestrian occurring boundaries, calculated
from the annotated data in the previous subsection,
we obtain the green dots, representing pedestrian de-
tections in reasonable and allowed positions in the
image. We do notice that this allows us to drop a
significant amount of false positive detections. Sub-
sequently we force the green borders on top of the
green data, demanding that our detections also fit our
height-position relation created from the manually an-
notated data. This in return removes a large part of the
false positive detections, keeping only the red detec-
tions as acceptable pedestrian detections.
4.1.4 Updating the Distribution Constraint
We acknowledge that assuming a Gaussian distribu-
tion around the fitted linear height-position relation
might not always be the best choice, especially when
you consider the fact that when moving further from
the car, differences in pedestrian height become less
obvious to notice, certainly at pixel level, whereas
close to the car height differences are clearly visible.
Therefore we updated the green borders, to closely
Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images
(a) Ground truth annotations with general and narrow bounds. (b) Detections with pruning steps applied.
(c) Applying narrower bounds. (d) Score Threshold and smallest detection size.
Figure 4: The height-position relation building process.
map the correct distribution of the annotation data,
which can be seen in Figure 4(a) as the magenta bor-
ders. Applying those updated constraints on the ac-
tual detection output, again removes several false pos-
itive detections, resulting in the magenta colored de-
tections seen in Figure 4(c).
4.1.5 Detection Certainty Thresholding
Before applying all the constraints defined in the pre-
vious subsections on top of the detection output, we
decided to put the detection certainty threshold very
sloppy, to ensure that the amount of false negative
detections is close to 0%. Now that we have auto-
matically removed multiple false positive detections,
we can look back to this setting and adapt it to our
application-specific needs. Due to less cluttered im-
ages filled with detections, since most false positives
are removed now, it becomes easier to select a decent
score threshold for our application. From experience
in using pedestrian detectors in the wild, we learned
that the used LatentSVM4 detector almost never re-
turns valid pedestrian detections when the certainty
score is below 0. Of course this value is application
specific and can change drastically when considering
other application fields. In our application, detections
with lower scores mainly resemble objects that have
similar feature descriptions, like a smaller tree or a
traffic sign, but in 99% of the cases, they do not match
actual pedestrians. Since we want to avoid blurring
too much valuable image information, we enforce an
extra pruning rule, demanding a detection certainty
score equal or above 0. This results in the black de-
tections, seen in Figure 4(d).
4.1.6 Visually Verifying the Filtered Detections
When visually checking the data, we wondered why
very small pedestrians in the background where ig-
nored by the pedestrian detection interface. As seen in
Figure 4(d) we calculated the smallest retrieved detec-
tion height by the DPM detector, which had a height
VISAPP 2016 - International Conference on Computer Vision Theory and Applications
of 105 pixels. Considering this in relation to the pre-
trained pedestrian model, this is actually normal, be-
cause the model is trained with a fixed training sample
height of 124 pixels, keeping a small area of back-
ground around the pedestrian, also called padding. At
detection time, the model’s dimensions always limit
the smallest possible detection height, so if we would
like to include these smaller pedestrians, we should
first upscale the input data. However we should keep
in mind that this introduces image artifacts which
could interfere with the pedestrian detector. In our
application this is no problem, since pedestrians with
a height smaller than 100 pixels are already privacy
secure and impossible to recognize when looking at
the complete mobile mapping image of 8000 × 4000
pixels (Tanaka et al., 2015).
4.2 Color-based Removal of
Pedestrian-like Detections
Even with all the proposed post-filtering steps ap-
plied, we noticed that some object classes contin-
uously succeeded in triggering false positive detec-
tions. Take for example the case of small traffic signs
indicating the traffic flow when entering a round-
about, as seen in Figure 5. As humans we clearly see
the difference between a pedestrian and this rigid traf-
fic sign. However due to the specific nature of pedes-
trian detection algorithms, it is normal that these false
positive detections occur. First of all, the used al-
gorithm (Felzenszwalb et al., 2008) ignores color in-
formation, since the variety of color in pedestrians is
enormous. Secondly as feature it uses edge informa-
tion of deformable parts. And this is exactly where
the biggest problems occur. The mentioned traffic
sign has a top part that is very similar to a head and
a middle and bottom part that have similar feature re-
sponses as a human body. Since the body and the head
are parts with a big weight in part-based pedestrian
models, it is important to add an extra pruning step
to remove these false positive detections that are still
classified as valid detections by our pipeline. Espe-
cially in the context of mobile mapping it is important
that crucial road information is not filtered or blurred
out due to privacy reasons, because many clients in-
Figure 5: Example of the need of an extra filter for traffic
signs still passing the post-processing steps.
(a) Positive training set.
(b) Negative training set.
(c) Test set + Classification result (green = sign /
red = pedestrian).
Figure 6: Positive, negative training and test set for traffic
sign filtering.
terested in this data are looking for exact locations of
traffic signs like this, e.g. to keep an automated index
of road sign conditions.
To avoid these kind of problems we propose a sim-
ple pruning step using a small Naive Bayes classifier.
This machine learning technique takes a limited set
of positive and negative training samples and, based
on some very simple color-based features calculated
from the training data, decides whether a valid de-
tection should still be classified as pedestrian or not.
We prefer using a machine learning approach towards
setting hard thresholds on basic features, because it is
more robust in finding the optimal separation between
classes once more training data is supplied.
As seen in Figure 6, we use a small positive and
negative training set (both containing only 5 samples),
where we tried to include as much traffic sign like
pedestrians in the negative set as possible (by look-
ing for matching colors), to avoid that those would
now get filtered out, e.g. when someone is wearing
a bright jacket. Finally we constructed a small test
set to evaluate the success rate of our classifier. From
each training sample a set of simple visual features
are calculated. In this case the most distinct feature is
the bright yellow color of the ‘body’ part of the traf-
fic sign. We separate the top 30% of the image and
then split up the bottom 70% in 3 equal regions, as
Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images
(a) (b) (c) (d) (e)
Figure 7: Naive Bayes Features: (a) original (b) CMY(K)
(c) C response (d) M response (e) Y response.
seen in Figure 7(a). middle area is then transferred to
the CMYK color space (Figure 7(b)) because the traf-
fic sign has a very low response in the C layer (Figure
7(c)), an average response in the M layer (Figure 7(d))
and a high response in the Y layer (Figure 7(e)). This
behavior is not equal for pedestrians. We take the av-
erage CMY values for this smaller window and use
that as feature vector for each positive and negative
sample. The K channel is simply ignored.
Finally when running the classifier on the test set
provided, the samples were all classified correctly ei-
ther as pedestrian or as traffic sign and thus the simple
classifier proved to work as an effective post-filtering
step. Similar behavior was detected for specific kind
of bushes, again in this case, an extra small Naive
Bayes filter could be constructed. The advantage of
this approach is that at post-processing time, the cal-
culation of these extra filters is computationally very
cheap ( 1ms) due to the very simple features used and
thus a small cost for a better classification result.
4.3 Soft Blurring Approach
The final step of our proposed pipeline is to obtain
the valid detected pedestrian regions and apply a local
apply a privacy-safeguarding filter to them. In our co-
operation with mobile mapping companies it became
clear that they want to manually define which part of
the detection is being blurred. Therefore we provided
the option for both pedestrian and face region blur-
ring. An intuitive way to apply privacy-safeguarding
would be to apply a standard Gaussian blurring fil-
ter. One of the main downsides to this is the exis-
tence of very prominent edge artifacts which cannot
be removed, as seen in the left part of Figure 8. We
would prefer a blurring filter that is not as strong on
the edges, as seen in the right part of Figure 8, but
which is strong in the middle and softens up towards
the edges of a detection. This ensures privacy but the
end result is visually more pleasing.
Instead of convolving the image region with a
Gaussian kernel with a fixed size and sigma, we pro-
pose a convolution with an adaptable Gaussian ker-
Figure 8: Blurring filters (left) standard Gaussian blur
(right) smooth blurring filter.
nel, where the sigma (σ
) is defined as a function
of the normalized pixel distance Ψ to the center of
the detection itself as described in equations (1), (2)
and (3). To ensure that the blurring is proportional for
differently sized pedestrian detections, we add an ex-
tra size dependency , which takes into account the
area of the detection found compared to the area of
the original image. This ensures that in the end each
detection is equally blurred.
Ψ = 1
, position)
= 0.1 + (∆Ψ
) (3)
We apply this soft blurring filter to every pedes-
trian detection in a given input image, blur out the
detected pedestrians or their associated face region
and make the captured mobile mapping image pri-
vacy safe. In our application we applied face blurring
which can be seen in Figure 10 and 9. This is simply
passed as an extra parameter to our smooth blurring
function. In order to make the blurring regions more
visible we also visualized the actual detections.
Figure 9: Close-up of privacy smoothing using only the
face region of the detection.
VISAPP 2016 - International Conference on Computer Vision Theory and Applications
Figure 10: Applying soft blurring on filtered pedestrian detections, but limiting the blurring to face regions only.
We applied the same post-processing steps discussed
in the previous sections (enforcing valid pedestrian re-
gions, applying a height-position relation and adding
a scoring threshold) to the second dataset and got
similarly good improvements. Only the color-based
Naive Bayes classification was left out, since the spe-
cific object class (roundabout traffic sign) did not oc-
cur inside the second dataset. We did not explicitly
look into an object class specific Naive Bayes filter for
the second dataset, but if such false positive trigger-
ing object class would occur, one could simply train a
classifier for that class using our software. The result
of pruning the detection output can be seen in Figure
11, while visual results of applying these constraints
can be seen in Figure 13. Especially pay attention to
the false positive detections on the car that are disap-
pearing as well as some of the double detections.
In order to make sure that we actually achieved
an improvement over simply using the out-of-the-box
Figure 11: Applying all post-processing steps to the out-
put of the LatentSVM 4 INRIA based pedestrian detector
applied on dataset 2.
available pedestrian detection algorithm, we evalu-
ated the number of true positive, false positive and
false negative detections after applying the different
post-processing steps discussed in section 4. The re-
sult of this comparison can be seen in Table 1. Subse-
quently, using precision-recall curves, we visualized
the accuracy gained by applying the mentioned post-
processing steps to the detection results on dataset
2, which can be seen in Figure 12. Notice that an
out-of-the-box object detector already experiences a
large accuracy drop when doing cross-dataset eval-
uation, and that there is a substantial accuracy gain
when applying our post-processing steps. Since the
detection analysis for dataset 2, shown in Figure 11,
proves that the minimum object size found by the
used DPM model is 101 pixels, we ignored possible
ground truth annotations on pedestrians smaller than
Figure 12: Precision-Recall curves generated for dataset 2
with all post-processing steps applied and the reported ac-
curacy using the area under the curve measurement.
Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images
Figure 13: Example of applying post-processing steps to data from the second dataset. (top) original detections at score
threshold -1; (bottom left) after pruning; (bottom right) after height-position relationship and score > 0.
100 pixels, to make an as accurate precision-recall
curve as possible. The remaining false negative de-
tections are mainly due to people sitting on benches,
riding bikes or motorcycles, which are less likely to
get detected by the used pedestrian DPM model and
which is a known issue.
We do acknowledge that this solution is far from
100% fail prove. There are still some bottlenecks that
should be taken into account. The overall approach
is generated to improve the output of any available
pedestrian detector without retraining the actual DPM
model specific to the application. However, up till
now there is not yet a single of-the-shelf pedestrian
detector which is able to detect every single pedes-
trian out there in any given application, especially
when performing cross-dataset validation (Torralba
et al., 2011). While our approach focuses mainly on
improving the recall rate of our detector, as seen in
Figure 12, getting the precision of pedestrian detec-
tors to 100% in any given application is still a very
challenging task and an actively researched topic.
During this research we made a visualization
showing the influence of changing the threshold on
the detection certainty, from a very sloppy value to
a very strict value, in relation to the amount of false
Table 1: Comparison of TP, FP and FN values after each
post-processing step for first dataset. To obtain a clear ben-
efit of applying these techniques, we ran the original DPM
detector at a score threshold of -1 like in the visual results
shown in Figure 13.
DPM orig. 928 4159 349
After pruning 928 3182 349
After height-position 852 1015 384
positive detections produced. This clearly shows the
influence of changing this parameter in search of an
ideal setting for any given application. The video can
be found at:
The goal of this paper is to efficiently blur pedestrians
in mobile mapping images to avoid privacy related
issues while safeguarding as much image informa-
tion as possible. By using an off-the-shelf pedestrian
detector trained on a different dataset and setting a
sloppy confidence threshold, we proved that applying
efficient post-processing filters, based on application-
specific constraints, e.g. a height-position relation,
can greatly improve the detection outcome. In ad-
dition to the proposed height-position filtering step,
we supply additional easy to train lightweight Naive
Bayes filters for objects that still trigger false positive
detections, e.g. roundabout traffic signs, without the
need of large amounts of annotated training data.
We prove that in a specific situation, we can use
pre-trained pedestrian detection models, but, given
a limited amount of manual annotations on a situa-
tion specific dataset, we can boost the detection ac-
curacy enormously by exploiting scene-specific con-
straints, e.g a known ground plane assumption. Fi-
nally we proposed an efficient soft blurring alterna-
tive to a standard Gaussian blurring filter, for privacy
masking reasons, by adaptively changing the param-
eters of the Gaussian kernel used for the convolution
with the found pedestrian detections.
VISAPP 2016 - International Conference on Computer Vision Theory and Applications
Since the processing of mobile mapping images is
being done off-line, and time and resource manage-
ment was not the focus of this research, we do not
need to concern about running the detector on every
image location, which is computationally quite ex-
pensive. One could argue that running the out-of-the-
box object detector multi-scale on every image po-
sition is actually a waste of resources and computing
time. As future work we suggest to integrate our post-
processing steps inside the actual pedestrian detec-
tion algorithm, enormously reducing the processing
time needed for a single mobile mapping image. This
might open up the possibility to do the processing
on-line, while capturing the actual data. This would
be better for industrial partners, since privacy issues
would be solved completely, due to the privacy sensi-
tive data not being physically stored anymore.
Our application focuses on detecting pedestrians
walking on the modeled ground plane, which raises a
new problem. People standing on a balcony, sitting
on a bench, lying on the grass or driving a bike, will
not fit into this ground plane assumption and will thus
simply be filtered out by our approach. We could im-
prove our approach by using multiple detection mod-
els, for these different pedestrian classes and then ap-
ply separate post-filtering rules for each detector.
One could not disagree that even with the current
bottlenecks, that this work is valuable for people han-
dling privacy sensitive mobile mapping data. This re-
search allows users to automatically remove privacy
sensitive data from their captured datasets, without
the need of manually handling each image (which
would be very costly and time consuming). It allows
users to grab off-the-shelve available pedestrian de-
tectors, add them to the system, and use a limited
manual input in their application field to derive the
post-processing rules. This highly benefits the com-
panies because they do not need to put huge amounts
of time and resources into building an application-
specific pedestrian detector themselves, needing thou-
sands of pedestrians to be manually annotated.
This work is supported by the Institute for the Pro-
motion of Innovation through Science and Technol-
ogy in Flanders (IWT) via the IWT-TETRA project
TOBCAT and via the IWT-TETRA project RaPiDo.
We would also like to thank Vansteelandt BVBA
and Grontmij Belgium, the companies who provided
the cycloramic image datasets during these projects,
which were used to develop and test this approach.
Cho, H., Rybski, P. E., Bar-Hillel, A., and Zhang, W.
(2012). Real-time pedestrian detection with de-
formable part models. In IVS, pages 1035–1042.
Dalal, N. and Triggs, B. (2005). Histograms of oriented
gradients for human detection. In CVPR, volume 1,
pages 886–893. IEEE.
De Smedt, F., Struyf, L., Beckers, S., Vennekens, J.,
De Samblanx, G., and Goedem
e, T. (2012). Is the
game worth the candle? Evaluation of OpenCL for ob-
ject detection algorithm optimization. PECCS, pages
Dibra, E., Maye, J., Diamanti, O., Siegwart, R., and Beard-
sley, P. (2015). Extending the performance of hu-
man classifiers using a viewpoint specific approach.
In WACV, pages 765–772. IEEE.
ar, P., Belongie, S., and Perona, P. (2010). The fastest
pedestrian detector in the west. In BMVC, volume 2,
page 7. Citeseer.
ar, P., Tu, Z., Perona, P., and Belongie, S. (2009). Inte-
gral channel features. In BMVC, volume 2, page 5.
Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008).
A discriminatively trained, multiscale, deformable
part model. In CVPR, pages 1–8. IEEE.
Nakashima, Y., Koyama, T., Yokoya, N., and Babaguchi, N.
(2015). Facial expression preserving privacy protec-
tion using image melding. In ICME, pages 1–6. IEEE.
Panagiotis, I. (2015). Preventing privacy leakage from pho-
tos in social networks. In CCS2015. ACM.
Peng, P., Tian, Y., Wang, Y., Li, J., and Huang, T. (2015).
Robust multiple cameras pedestrian detection with
multi-view bayesian network. Pattern Recognition,
Puttemans, S. and Goedem
e, T. (2013). How to exploit
scene constraints to improve object categorization al-
gorithms for industrial applications. In VISAPP, vol-
ume 1, pages 827–830.
Tanaka, Y., Kodate, A., Ichifuji, Y., and Sonehara, N.
(2015). Relationship between willingness to share
photos and preferred level of photo blurring for pri-
vacy protection. In ASE BigData & SocialInformatics,
page 33. ACM.
Torralba, A., Efros, A., et al. (2011). Unbiased look at
dataset bias. In CVPR, pages 1521–1528. IEEE.
Van Beeck, K., Goedem
e, T., and Tuytelaars, T. (2012). A
warping window approach to real-time vision-based
pedestrian detection in a truck’s blind spot zone. In
ICINCO, volume 2, pages 561–568.
Viola, P. and Jones, M. (2001). Rapid object detection using
a boosted cascade of simple features. In CVPR, pages
Safeguarding Privacy by Reliable Automatic Blurring of Faces in Mobile Mapping Images