Detection of Scene Obstructions and Persistent View Changes

2012 15th International IEEE Conference on Intelligent Transportation Systems
Anchorage, Alaska, USA, September 16-19, 2012
Detection of Scene Obstructions and Persistent View Changes in
Transportation Camera Systems*
Ajay Raghavan, Robert Price, and Juan Liu
Two important classes of problems that affect such fixed
camera systems are obstructions and persistent view changes
from causes such as undesirable tilting of the camera.
Obstructions can cause the transportation scene of interest to
be partially blocked or out of view. These can result from
various factors: spray paint or blockage due to vandalism,
foliage growing into the scene, ice forming on the porthole in
the winter, construction signage, soot, etc. In addition, for
fixed cameras, maintaining its orientation is important to
ensure that the scene of interest is framed correctly. Often
times, subtle unintentional tilting of the camera can cause
certain scene elements of interest (e.g., a turn lane) to go out
of view. Such tilting from the nominal orientation can be
caused by factors such as technicians cleaning the deviceviewing porthole periodically, intentional vandalism, and
accidental collisions with vehicles. On some occasions, new
scene elements of interest might appear and cause persistent
view changes. Examples include stalled cars, suspicious
packages, or an accident. These might not be problems
affecting the camera function per se, but might be cause for
alerting operators, particularly in surveillance applications.
Abstract— Unattended camera devices are increasingly being
used in various intelligent transportation systems (ITS) for
applications such as surveillance, toll collection, and photo
enforcement. In these fielded systems, a variety of factors can
cause camera obstructions and persistent view changes that
may adversely affect their performance. Examples include
camera misalignment, intentional blockage resulting from
vandalism, and natural elements causing obstruction, such as
foliage growing into the scene and ice forming on the porthole.
In addition, other persistent view changes resulting from new
scene elements of interest being captured, such as stalled cars,
suspicious packages, etc. might warrant alarms. Since these
systems are often unattended, it is often important to
automatically detect such incidents early. In this paper, we
describe innovative algorithms to address these problems. A
novel approach that uses the image edge map to detect nearfield obstructions without a reference image of the
unobstructed scene is presented. A second algorithm that can
be used to detect more generic obstructions and persistent view
changes using a learned scene element cluster map is then
discussed. Lastly, an approach to detect and distinguish
persistent view changes from changes in the orientation of the
fixed camera system is explained. Together, these algorithms
can be useful in a variety of camera-based ITS.
In order to implement automated checks for the
occurrence of the above-mentioned problems and incidents, it
is desirable to use algorithms that can operate without the
need for the full reference image of the same scene without
the problem/incident. As one can imagine, transportation
scenes captured by fixed cameras are constantly changing
due to vehicles, pedestrians, seasonal and daily lighting
changes, weather elements, and longer term changes in
background elements (e.g., trees shedding leaves, changing
billboards, etc.) Therefore, it is typically not practical to
compare identical images with and without particular
problems. Therefore, there is a need for no-reference or
reduced-reference image quality algorithms that can detect
these problems. Among works that have examined camera
diagnostics for image quality, Hallowell et al. [1] explored
the use of surveillance cameras for airport visibility-related
weather and roadway condition monitoring applications.
They discussed the concept of composite images for a camera
aimed at a site that used a map of “expected” edges based on
a series of training images for that site and ignored
“transient” edges to avoid data quality issues from
obstructions such as rain drops and dust. Harasse et al. [2]
discussed the problem of detecting surveillance video camera
issues such as obstructions, displacements, and focus issues
using a similar idea of “stable” edges (also learned for each
site) and examining their characteristics as they change from
frame to frame. In this respect, it is arguably easier to deal
with a continuous stream of video frames where one has the
previous video frame as “reference” to examine for
significant, abrupt changes. The algorithms presented by us
in this paper are not restricted to such camera devices, but are
I. INTRODUCTION
Unattended fixed camera-based devices are now key
components of various intelligent transportation systems.
They find application for automated toll collection, traffic
incident monitoring, and photo enforcement of various traffic
rules, such as red light, speed limits, and stop signs, among
others. Since these systems are unattended fielded systems, it
is important to periodically check them for problems and
examine for potential image/video quality problems that
might interfere with their intended functionality. Without
such checks, downtime and device degradation losses can
quickly add up to substantial portions of operating expenses.
Presently, various intelligent transportation solution service
providers do such checks manually. As the fleet of fielded
camera devices increases, these checks can contribute to
significant operational overhead; therefore, there is interest in
automating such checks. In addition, for surveillance
applications, persistent scene element changes are of interest
to detect suspicious activity or backups due to accidents and
other incidents warranting attention. Automated detection of
such incidents can help operators monitoring these cameras
manage a larger fleet of camera devices without accidentally
missing key events.
*Research supported by Xerox Corporation.
Ajay Raghavan#, Robert Price, and Juan Liu are with the Palo Alto
Research Center (PARC, A Xerox Company), 3333 Coyote Hill Road, Palo
Alto, CA 94304, USA (#Corresponding author phone: 1-650-812-4724; email: {raghavan, bprice, jjliu}@parc.com).
978-1-4673-3063-3/12/$31.00 ©2012 IEEE
957
from the particular camera being monitored. It uses edge
detection [7] over the image captured by the camera and
computes the local edge density over an appropriately sized
window. The key assumptions made are: a) the scene of
interest is largely in focus under nominal conditions.
Consequently, scene features are sharply captured and the
image exhibit a certain homogeneity in edge density or more
generally regions with high local gradients or sharper focus,
and b) an obstruction is in the near field compared to the
focusing range (see Fig. 1) and hence not in focus.
Consequently, the features of the obstruction are blurred,
and there is a significant reduction in edge density or a local
focus metric. Given these assumptions, we propose an image
quality metric that computes edge density of an observed
image. The edge density metric can then be compared to a
threshold value that is learned from a nominal set of images
to detect near-field obstruction. Figs. 2 and 3 show a couple
of representative images with scene obstructions and their
corresponding edge maps. Thus, the aforementioned
assumptions are satisfied for a wide variety of obstructions
commonly encountered by fixed transportation cameras and
are extendable to pan-zoom-tilt cameras since it does not
need a reference image. Thus, the edge density metric is a
fast and efficient way for detecting such blockage patterns.
Figure 1. Camera device imaging typical scene and near-field obstruction
also applicable to devices capturing photographs and/or video
clips of incidents intermittently when triggered by vehicle
detection or other events of interest.
Automated detection of persistent changes caused by
incidents of interest is useful in many domains. For instance,
one may wish to detect the introduction of a suspicious piece
of luggage (a persistent change) against the background scene
of a busy airport (many transient changes). Many cameramonitoring systems provide functions to detect transient
changes, but the detection of persistent changes in
dynamically varying scenes is still largely performed
manually by human agents examining images and videos.
The manual process is tedious and error prone. Automated
site-specific persistent change detection is thus a subject of
significant interest. However, site-specific persistent change
detection in outdoor scenes with their naturally varying
illumination due to sun position and weather effects is
difficult. The introduction of local distribution models and
intensity-invariant image spaces have been found to partially
address some of these challenges [3]. The segmentation of
images into regions and the detection of changes in images
are both well-explored topics in computer vision. For
instance, an SVM classifier has been used to classify pixels
based on color for classifying image segments into sky and
non-sky for aircraft monitoring [4]. This method is simple,
but requires training with labeled regions of the sky. Many
algorithms have been proposed in the domain of security
monitoring. Typically these are based on either simple frame
differences [5] or incorporate specific dynamic models for
tracking of people [6]. Algorithms that can find persistent
changes in scenes without the need to train specific models
for specific types of objects are desirable, since we may not
know a priori what type of changes we are looking for.
B. Implementation for Obstruction Detection and Training
Fig. 4(a) shows the computation of edge density metric.
First the image goes through an edge detector to generate an
edge map. In practice we favor the use of gradient-based
edge detector such as Sobel edge detector over soft-edge
detector such as Canny edge detector [7]. The latter can
detect softer edges (i.e., edges with lower gradient intensity)
attached to harder edges and enforce edge continuity. In the
present context, this turns out to be disadvantageous, since
obstructions within subtle features such as tree leaves might
be detected, which is undesirable.
The second step uses a local window of suitable size (a
500 pixel square worked well on images of size 2700 × 4080)
to find larger local pixel neighborhoods of the image without
edges. For any given pixel (i, j), it is labeled 1 if there is an
edge pixel within the local window centered at (i, j), and
labeled 0 otherwise. The local windows used for averaging
are shown on the edge maps using dashed red boxes in Figs.
2(b) and 3(b). This generates a binary image where pixel
values indicate local edge content. The algorithm then applies
connected component analysis to identify clusters of pixels
corresponding to edge-free regions. We then summarize the
overall edge content as a scalar value, which measures the
percentage of edge-free regions. Fig. 4(b) shows the learning
phase. The input is a set of images, nominal and/or with
blockage. The edge density metric is computed for each
image. The edge metric values are then analyzed over this
training set to get its nominal distribution. Such statistics are
used to determine the proper threshold value (3-5% worked
well for daytime images). Blockage detection is fairly simple:
the overall edge density metric is compared against the
learned threshold and an alarm is raised if the former is
lower. This approach was validated over a set of 23 examples
of traffic scenes with a variety of obstructions; of these, only
3 examples of non-homogeneous obstructions were not
In the rest of this paper, we present three algorithms that
address these highlighted issues. First, a novel approach for
reference-image independent near-field obstruction detection
is discussed. The second algorithm relies on characterizing
the scene of interest based on feature-level descriptors into
classes (without any manual tagging) and detects persistent
view changes by looking for class changes at the featurelevel. The third algorithm can detect shifts in the framing
based on its static scene elements. Each of these is described
in the following sections.
II. REFERENCE IMAGE-FREE OBSTRUCTION DETECTION
A. Edge Density Metric for Near-Field Obstructions
To address the above-mentioned blockage problems, we
propose a near-field scene obstruction detection method that
does not require a reference image of the scene of interest
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successfully detected (discussed next). In addition,
approximately 200 examples of unobstructed traffic scenes
were tested under various traffic and lighting conditions and
false alarms were not raised (except for foggy days). These
outlier cases are discussed next.
feature clusters is described next. Such methods would fall
into the class of “reduced-reference” image quality
algorithms, In such approaches, while the original image of
the scene without degradation is not needed, some of its
reduced features are extracted and used for subsequent
quality analysis by comparing them with the respective
features from the test image.
The assumption that the region of interest of the image
scene exhibits homogeneity in edge density is not always
true. For instance, sun glare, overexposure, and scenes with
significant portions of clear blue skies during daytime may
reduce the overall or local edge density (see Fig. 5). In our
implementation, we identify sun glare and overexposure
problems and do not check for near-field blockage in those
(minority) cases to avoid false alarms. Sun glare and
overexposure problems can be detected by looking for
saturated portions in the image (see Fig. 5(c) and 5(d)).
Blue-sky regions are identifiable by their distinctive color
signature. In addition, daytime versus nighttime
classification needs to be done in the pre-processing stage
(addressed by us in [8]): this is because nighttime images
tend to have larger regions without features due to lack of
ambient lighting and therefore need larger thresholds for
obstructions (at the cost of lower detection sensitivity).
(a)
(c) Deduced binary obstructed
region map overlaid on
grayscale original image
(b)
Figure 3. (a) Foliage growth into traffic intersection scene causing
partial obstruction over region of interest; (b) Sobel edge map of image
in (a), and (c) Deduced obstruction map
(a)
(c) Deduced binary obstructed
region map overlaid on
grayscale original image
(b)
Figure 2. (a) Transportation scene captured by a camera with deliberate
obstruction introduced over porthole; (b) Sobel edge map of image in (a),
and (c) Deduced obstruction map
It should be mentioned that there are some limitations of
this method. Traffic scenes captured on foggy days do tend
to lack sufficient hard edges. However, it should be added
that on such days, the cameras may or may not capture poor
quality images of some of the vehicles, as shown in Fig.
6(a). This would be an example of an uncontrollable
obstruction in which case, the algorithm alerts to the fact that
the camera may not be able to capture the scene of interest
with desired clarity. In addition, the algorithm can miss
some subtle obstructions that do not cause significant drops
in edge density, such as in Fig. 6(b). However, such
obstructions are less common in transportation applications.
Arguably, some of these obstructions cannot be detected
without at least some site-specific training done a priori for
the algorithm. An example of such an algorithm that can
learn the composition for a specific scene by grouping it into
(a)
(b)
Figure 4. (a) Flowchart for computation of edge density metric; (b)
learning to obtain threshold value from a set of training images
III. PERSISTENT SCENE CHANGE DETECTION THROUGH A
REDUCED-REFERENCE METHOD
To address the above-mentioned persistent view change
detection problem, we propose a reduced-reference
algorithm in this section. The key requisite element here is a
method of filtering out transient changes to leave behind
persistent changes. One way to separate these types of
changes is to exploit their time scale. We classify changes
into transient changes lasting a short duration and persistent
changes that are visible over an extended time. In the
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(a)
(b)
(c)
(d)
Figure 5. Some issues that need to be checked for during preprocessing
before the edge density method can be used for obstruction detection: (a)
Sun glare in the scene and (b) Overexposure; (c) Histogram of top quarter of
image in (a) that indicates sun glare based on % of pixels clipping/saturated;
(d) Histogram of overall image in (b) that similarly indicates overexposure.
following sections, we show how to combine this idea of
filtering at various time scales together with methods for
abstracting pixels into classes to cope with variations in
illumination and appearance to create a robust mechanism
for persistent change detection in dynamically changing
scenes. The algorithm proposed in this section can detect
both scene obstructions and other persistent scene view
changes (the latter not necessarily being in the near field of
the camera). Subsequently, it can be used in conjunction
with the algorithm described in Section II to distinguish
between near-field obstructions and other persistent changes.
XYZ color space coordinates. L*a*b* space is preferred as
unit changes in L*a*b* space correspond to perceptually
salient changes in visual stimuli for the human visual
system. The L*a*b* space also separates out the image
intensity dimension from the hue component. This provides
a natural basis to provide intensity invariant features that are
robust to changes in illumination. In addition to local
intensity and hue features, we also provide a feature that
captures the local texture around the pixel. The current
embodiment employs the local entropy of pixel intensities in
each of the three L*a*b* components as a measure of local
texture. To compute point-wise entropy, the histogram is
obtained over a local neighborhood to get counts of various
intensities and then normalize these over the total number
pixels in the neighborhood to get probabilities. The entropy
of this distribution is a measure of how uniform the
neighborhood of the pixel is. Finally, the x and y coordinate
of the pixel are used to associate certain regions with
obstacles and to encourage smoothness of classification. The
result is a vector V of pixel features with one row per pixel
in the image, each of which has the form:
∗
∗
∗
∗
! = !, !, !∗(!,!) , !(!,!)
, !(!,!)
, ! !∗(!,!) , ! !(!,!)
, ! !(!,!)
(1)
The abstract pixel features capture important properties of
the pixels but are still hard to compare across images. For
instance, a tree might have small variations in pixel hue and
texture across images, but this is not significant. In the next
step, we quantize or cluster these pixel features into a small
number of classes that are robust to local changes. We
accomplish this by grouping the image features as a mixture
of Gaussian components. The location features provide useful
additional features in the context of pixel clustering. The
location parameter also forces generalization of the Gaussian
components. Given several images of the same intersection
from a fixed camera, we would expect a particular pixel to be
generated by the same object class. The Gaussian
components must therefore orient themselves to handle
variations in lighting across the scenes over different days
and seasons. A foliage pixel has to represent a range of
appearances of the same leaf object. A roadway pixel must be
broad enough to represent both road and passing cars. The
result of the Gaussian mixture segmentation is the
classification of pixels in the original image into a few large
regions corresponding to different types of objects. These
tend to group things like foliage, sky, roads and cars into
clusters. Note that manual cluster tagging is not needed.
(a)
(b)
Figure 6. Examples of some limitations of proposed reference-image
independent edge density-based obstruction detection method: (a) Fog in
the scene that is likely to be detected as an obstruction (but might warrant
an alarm all the same since vehicles cannot be seen clearly in such
situations) and (b) Small tree with low density that cannot be detected
A. Pixel Features
Our solution to this problem has two main components.
The first component computes a new representation of the
image in an abstract feature space. The features represent
pixels in terms of intensity, hue, texture and location. These
features naturally discriminate between objects of interest.
Foliage tends to have predominantly green and brown tones
and has a richly textured appearance. It is generally around
the outsides of the image. Automobiles tend to have lighter
colors and smooth textures. They tend to be in the middle of
the picture. The abstraction process starts with conversion of
the RGB image from the camera into L*a*b* space. The
L*a*b* color space is a well-known color-opponent space
with dimension L* for lightness and a* and b* for the coloropponent dimensions, based on nonlinearly compressed CIE
In Fig. 7, the pixel features found in a set of training
images have been clustered into two classes corresponding
to “low lightness objects with lots of texture” and a “high
lightness region of pixels with a broad range of textures”
ranging from smooth to rough. Here texture is captured by a
local entropy calculation. Given a pixel from a new image,
we can calculate its features in this space and then examine
which cluster it is closest too. This cluster gives us the class
of the pixel. In this example, the new point is closest to the
cluster on the right, namely the brighter objects with a
variety of textures, so it would get the label of this group.
When we are classifying pixels in a new image that was not
present in the training set, it can happen that a pixel very far
960
Figure 7. Frequency of image pixel types by brightness L and local
entropy H(L). Higher count frequencies are indicated by red while lower
ones are indicated by blue.
(a)
(b)
(c)
away from any cluster, or is roughly equal distance from
several clusters. In this case, we will be unsure which cluster
it belongs to. We assign these pixels the “unknown” class.
In our experiments on detecting persistent changes in
transportation images, we found that a Gaussian mixture
with 5 components, corresponding to five possible pixel
(d)
types provided sufficient distinctions to detect the kind of
changes that we are concerned with. Applied to our Figure 8. (a) – (c): Three training images converted to abstract pixel class
representations; (d) Persistent reference feature-class level image generated
transportation domain, we obtain images like those in Fig. 8
from images in (a) through (c)
(a)-(c). The top of each frame contains the original RGB
image. The bottom of the frame contains a false color image
actually sitting over top of a transient change in the original
displaying the abstract class of each pixel. We can see that image set (there is a car gray car in the far left lane in the first
distinct classes of pixels are separated in the images image of the training set). The persistent reference model and
the persistent snapshot are shown in Fig. 10. Traditional
corresponding to distinct elements (sky, trees, road, and car).
background subtraction methods can then be used on the
B. Persistent Change Detection
abstract persistent reference and test images to detect changes
Given an image represented in terms of abstract pixel in the scene between camera setup and the current time. A
classes, the next step is to differentiate transient and threshold can then be applied to the persistent differences
persistent changes. First, we create a “persistent reference image to detect changes of sufficient size and intensity to be
image”. When the camera is setup at a new installation, we worth notifying a human operator that a review is required. In
gather a number of images (~5 to 10) of the same scene taken Fig. 10, we can see that the persistent vehicle appearing in the
from a fixed camera viewpoint and convert them into our left hand lane is clearly highlighted whereas the transient cars
abstract representation (Fig. 8). We can then apply a passing through the scene have been completely suppressed.
consistency operator (such as the statistical “mode” or
conjunction) to the sequence of abstractions to find the most
common class at each location. This is easily generalized to
moveable cameras (such as pan-tilt-zoom ones) by forming a
reference image for each of the camera viewpoints. In the
persistent reference image shown in Fig. 8(d), we have used
the set of abstract pixel class images above to generate a
persistent reference image. Even with only a few images, the
transient cars passing through the frame disappear, but
persistent features such as the trees remain clearly
segmented. This gives us a clear reference image free from
transient artifacts. This result is obtained without any human
labeling of images. An illustrative flow chart of the concept is
shown in Fig. 9.
During deployment, a set of images is taken to
characterize the current state of the intersection. Again each
of these images is converted into abstract pixel class images.
We can construct the persistent test image from the set of
abstract pixel class images by the same process described
earlier for training images. Again, the mode operator gives us
a stable picture free of transient automobile artifacts,
however, the persistent features of the image are preserved
(Fig. 10b). In this case, an automobile in the far left hand lane
persists for a large number of frames and is captured in the
abstract image. Notice that the persistent change detected is
Figure 9. Flowchart for computing persistent feature-level image from a
set of images for a scene from a single site.
Thus, the algorithm described in this section makes it
possible to automate the detection of persistent changes
occurring within images appearing anywhere within a largescale deployment of monitoring cameras. Furthermore,
configuring the detection system requires no specific
programming, model building or other input from the
961
operator. The system merely needs a few (5-10) images
across one or more days from each scene to be monitored. It
should be noted that in addition to obstructions and stable
scene element changes, the persistent view change algorithm
would also detect camera misalignment/tilting that should
shift the scene in the fixed camera view. To distinguish such
changes, another algorithm that relies on the location of
static scene elements in the view is briefly described next.
(a)
(b)
(c)
Figure 11. Illustration of misalignment detection algorithm: (a) Original
night-time region of interest within intersection scene for traffic signals; (b)
Binary image of candidate objects matching color template for red light
phase; (c) Filtering by size and other criteria preserve only the light phases.
− a)
b)
V. CONCLUSION
In summary, we presented three algorithms to address the
general problem of persistent view change detection for
transportation cameras. These algorithms do not need a full
reference image from the camera for evaluating a scene.
They have shown promising results in initial tests. They can
detect undesirable problems such as obstructions and
displacements, as well as alert to scene element changes of
interest from a traffic monitoring or surveillance perspective.
=
c)
Figure 10. Stable scene change (marked with blue patch) obtained using
proposed persistent view change detection algorithm.
IV. CAMERA MISALIGNMENT DETECTION
To detect unintended camera misalignments, we propose
the use of the location coordinates of automatically detectable
static scene elements. Since the scene captured by such
camera devices is dynamic and exhibits significant variations
over time, it is essential to rely on one or more elements that
are known a priori to be static and unchanging. For example,
in traffic intersection cameras, the traffic lights and signs,
road lane and stop bar markings are examples of naturally
occurring static scene elements. It is possible to train for
element-specific features (either human-perceived or
machine learned) that can isolate them; subsequently its
location(s) is used to detect camera misalignment.
ACKNOWLEDGMENTS
The authors gratefully acknowledge our project partners
within Xerox Transportation Services for frequent
consultations and database aggregation: Natesh Manikoth,
Marco Bressan, John Kowalsky, Todd Jackson, Juan Suarez,
Michael Peterson, Allison McCann, Thomas Wright, Adil
Attari, and Michael Shollenberger. We also appreciate the
support of our program managers at Xerox, Norm Zeck and
Kenneth Mihalyov and the early-stage project leadership of
Serdar Uckun (PARC, presently at CyDesign).
We have implemented algorithms to check for camera
misalignments using traffic light locations within the image.
In those, we start by detecting objects that match the color
templates for red, yellow, or green light phases (done over a
region of interest, say top quarter where signals are typically
framed for enforcement applications); typically multiple
templates are needed to accommodate a variety of ambient
lighting conditions. That is followed by filtering to remove
objects that fall outside learned size limits. Subsequently,
connected components that meet a particular circularity
criteria are preserved as candidate traffic light phases (Fig.
11). For example, for circular phases, a circularity metric C =
4πP/A (P is the object’s perimeter and A its area) is used
while for turn signals a template match is used. If these
detected candidate light phase objects consistently do not
match one or more of the marked signal phase locations
during training within some tolerance (to avoid false alarms
from wind oscillations), the camera is judged to be out of its
nominal alignment. This concept has been verified over an
initial database made available to us by our project partners
of approximately 500 intersection images taken under a broad
variety of lighting conditions (day/night) at various sites for
accurately detecting misalignments using the traffic lights.
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