=Paper=
{{Paper
|id=None
|storemode=property
|title=Mobile Objects and Sensors within a Video Surveillance System: Spatio-temporal Model and Queries
|pdfUrl=https://ceur-ws.org/Vol-1075/05.pdf
|volume=Vol-1075
|dblpUrl=https://dblp.org/rec/conf/immoa/CodreanuMS13
}}
==Mobile Objects and Sensors within a Video Surveillance System: Spatio-temporal Model and Queries==
https://ceur-ws.org/Vol-1075/05.pdf
Mobile objects and sensors within a video surveillance
system: Spatio-temporal model and queries
Dana Codreanu, Ana-Maria Manzat, Florence Sedes
Université de Toulouse – IRIT – UMR 5505
118 Route de Narbonne, 31062 Toulouse Cedex 9, France
{codreanu, manzat, sedes}@irit.fr
ABSTRACT available for all these videos are the id of the camera (eventually
The videos recorded by video surveillance systems represent a key GPS coordinates) and a local date/timestamp that are not
element in a police inquiry. Based on a spatio-temporal query homogenous throughout the different systems.
specified by a victim, (e.g., the trajectory of the victim before and A great majority of the existing video surveillance systems
after the aggression) the human operators select the cameras that are manual or semi-automatic (they employ some form of video
could contain relevant information and analyse the corresponding processing but with significant human intervention) [11]. Taking
video contents. This task becomes cumbersome because of the into account the huge amount of video contents that need to be
huge volume of video contents and the cameras’ mobility. This handled, the purely manual approach (agents watching the videos
paper presents an approach, which assists the operator in his task and detecting events) becomes insufficient. The main objective in
and reduces the research space. We propose to model the the video surveillance domain is to provide users with tools that
cameras’ network (fixed and mobile cameras) on top of the city’s could assist them in their research by reducing the research space
transportation network. We consider the video surveillance system and therefore the response time. These tools depend on the
as a multilayer geographic information system, where the cameras research context and complexity (e.g., real time surveillance of big
are situated into a distinct layer, which is added on top of the events, police inquiry) [22].
other layers (e.g., roads, transport) and is related to them by the
location. The model is implemented in a spatio-temporal database. Our work is situated in the context of the police inquiry
Our final goal is that based on a spatio-temporal query to which involves an a posteriori processing of the data in order to
automatically extract the list of cameras (fixed and mobile) help the investigator to highlight (isolate) the relevant elements
concerned by the query. We propose to include this automatically (e.g., persons, events). To do that, the investigators dispose of the
computed relative position of the cameras as an extension of the set of recorded videos from different video surveillance systems
standard ISO 22311. (e.g., public, private, RATP). In order to assist the investigators in
their tasks, it is important that the different outputs of the systems
are interoperable, which is not currently the case. The
interoperability between any video surveillance systems from the
1. INTRODUCTION simple ones with only few cameras to the large scale systems is
The number of video surveillance cameras increases in the main goal of the standard ISO 223111. It specifies a format for
public and private areas (e.g., in train and metro stations, on- the data which can be exchanged between the video surveillance
board of buses and trains, inside commercial areas, inside systems in the inquiry context.
enterprises buildings). For example, some estimations show that This standard does not consider the video surveillance
there are more than 400000 cameras in London and that only the cameras’ mobility or their fields’ of view modification. In fact, at
RATP also known as Régie Autonome des Transports Parisiens the beginnings of video surveillance systems the cameras were
(English: Autonomous Operator of Parisian Transports) placed in fixed locations in order to monitor indoor and outdoor
surveillance system comprises around 9000 cameras in Paris. In places. With the improvements in the hardware and software
these conditions, any person that lives and walks in those two big technologies, on-board cameras are more and more employed in
European capitals is likely to be captured many times during a day mobile vehicles (e.g., buses, police cars). This cameras’ mobility
(up to 300 times in London) by several video surveillance systems makes the task of security agents even more difficult in the
(e.g., the traffic surveillance cameras, the cameras in the subway, context of an inquiry, when they have to analyse a huge amount of
and the cameras of a commercial centre). The only markers video contents and to have supplementary knowledge on the
system’s characteristics (e.g., the bus timetables, the city transport
plan) in order to select the most appropriate video contents.
In this context, our goal is to provide users with tools that
could assist them in their research and reduce the research space.
In order to achieve this objective, in this article, we propose an
extension of the ISO 22311 standard in order to take into account
1
http://www.iso.org/iso/fr/catalogue_detail.htm?csnumber=5346
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the cameras’ mobility. We consider the video surveillance system Camera type: optical, thermal, infrared
as a multilayer geographic information system, where the cameras Sensor type and dimension: CMOS, CCD
are situated on a distinct layer, which is added on top of the other Transmission type: analogous/ IP
layers (e.g., roads, transport) through the location. We Angle of view (horizontal and vertical), focal
implemented our solution using a spatial database in order to distance, pan-tilt-zoom, field of view orientation,
select the cameras that might have acquired video contents visible distance etc.
corresponding to a user’s spatio-temporal query.
The remainder of this paper is organized as follows. After a
review of related work concerning the three aspects addressed in
this paper, video surveillance systems, standard ISO 22311 and
mobile objects modelling in the Section 2, Section 3 presents our
multilayer modelling approach. This model is implemented using
a spatio-temporal database. Some queries that can be answered
based on this database are presented in Section 4. Finally, Section
5 concludes and discusses possible future research.
Figure 2: Examples of video surveillance cameras having
the same position but different fields of view
2. STATE OF THE ART We started by analysing the way a query is processed in a
2.1 Video Surveillance Systems video surveillance system today. When a person (victim of an
The generic schema of a video surveillance system is aggression for example) files a complaint, he is asked to fill a
illustrated in Figure 1. The content is captured and stored in a form describing the elements that could help the investigators to
distributed manner and analysed in a control centre by human find the relevant video segment (the Figure 3 illustrates an
operators that watch a certain number of screens displayed in a example of such form). Based on the spatial and the temporal
matrix (the Video Wall in Figure 1). aspects of the query, the surveillance operator uses his own
knowledge concerning the spatial disposal of the cameras’
network in order to select the most relevant video contents. Then
he analyses these contents by playing them on the different
screens that he has in front of him. The monitors themselves show
no spatial relationship of any kind, only the numbering of the
cameras is in a somewhat logical order.
Figure 3: Example of a form filled by a victim
Figure 1: Video surveillance system’s schema Therefore, the operators’ tasks become cumbersome taking
into consideration the huge volume of video contents to be
analysed, the mobility and the different characteristics of cameras.
There is a big diversity of cameras and sensors that constitute Moreover, in the current systems, most of the stored contents is
the acquisition part of surveillance systems and a heterogeneity of not exploitable because of the recording’s low quality. This lack
their installation contexts (e.g., on the halls or platforms of of quality is often caused by inappropriate installation of cameras,
railway or metro stations, on-board of trains and buses, on the bad shooting, bad illumination conditions etc. The operator has no
streets, in commercial centres or office buildings). Therefore, we a priori knowledge on the quality of the video contents and thus
have fixed and mobile cameras having different technical he loses time by visualizing the low quality contents also.
characteristics (most of the time dynamic) (see Figure 2 for an
example of such cameras) [14]:
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Figure 4: ISO 22311 sensor description
The video surveillance domain has seen a big number of for the data issued from video surveillance systems and the
commercial systems developed [8]. In the research area, many metadata needed to exploit that data.
projects were developed as well: CROMATICA [5],
In the following, we are going to present the ISO 22311
CARETAKER2 [3], VANAHEIM3 for the indoor static video
standard, especially the part concerning the description of the
surveillance, and SURTRAIN [20], BOSS4 [13],
5 cameras characteristics and mobility. We are going to highlight
PROTECTRAIL projects for the on-board mobile surveillance.
the interesting elements which relate to our research.
All these heterogeneous projects concentrate on the development
of the system’s physical architecture and of better detection
algorithms in order to obtain a fully automatic system [12], [24].
We can summarize by saying that there is a growing concern
2.2 Standard ISO 22311
The Standard ISO 22311 defines an interoperability format
in the research and industrial environments for developing
for the data generated by video surveillance systems and for the
algorithms for video content analysis (VCA) in order to
metadata needed to exploit these huge volumes of data.
automatically index content and detect objects (e.g., abandoned
packets or luggage) and events (e.g., intrusions, people or vehicles The audio visual packages (containing audio, video or
going the wrong way) [16] or to draw operators’ attention to metadata files) have to be structured hierarchically (in files,
events of interest (e.g., alarms). However, solutions for assistance folders and groups of folders) according to time intervals in
to a posteriori investigation are at a lesser stage of maturity, and to Coordinated Universal Time (UTC). For each group of folders it
date most of the data remain unexploited. is mandatory for the system to provide a XML description of the
source(s) (e.g., cameras, GPS, video analysis tools), codec(s), file
In this article, we are going to address also the lack of
formats and a temporal index enabling an easy access to the
interoperability between different surveillance systems. In the
content.
context of an inquiry, the police might need to analyse data from
different sources (systems), so it is important that the different The current technologies and processing power enable the
outputs of the systems to be interoperable. As a consequence, the analysis of video content and the extraction of metadata
big actors of the domain started to unify efforts in order to describing objects, events, scenes etc. This analysis depends on
standardize the structure of folders and of metadata files generated the acquisition context (e.g., the position of the camera, the image
by video surveillance systems. A result of these efforts is quality, the type of sensors). Therefore, the standard distinguishes
represented by the ISO 22311 standard that proposes a structure between the systems, those that can generate such metadata (i.e.,
level 2 systems) and provides a general structure and dictionary
for describing sensors and events (i.e., metadata).
2
http://cordis.europa.eu/ist/kct/caretaker_synopsis.htm As in this paper we are going to address the problem of
3
http://www.vanaheim-project.eu/ cameras’ geo-localization we present the schema for the sensors
4
http://celtic-boss.mik.bme.hu/ description in Figure 4.
5
http://www.protectrail.eu/
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Each camera has an absolute location (GPS coordinates) as defined by [9] (e.g., moving points, moving lines, moving
more and more of the installed cameras have an embedded GPS regions), or using the dynamic attributes [23] (e.g., motion vector)
transmitter. But, there are many cases when the GPS is not which enables to limit the size of the data that has to be stored and
enough because: (1) we need to model the position of the camera queried.
with regards to the video surveillance system and not to the world;
As far as we know, the video content’s mobility is not taken
(2) in some situations, for example in indoor environments, the
into account in the video surveillance domain. In this article, we
GPS positions do not provide a good precision.
want to exploit the advances in the field of mobile objects and
In the context of a video surveillance system: apply them in the video surveillance domain in order to consider
the mobile aspect of surveillance cameras.
The mobile cameras are embedded in buses, train, police
cars;
The movement of these vehicles is constrained by a road
network and a transportation network. 3. Extension of the Standard 22311 for the
By analysing the standard, we can notice that it defines a management of cameras mobility
relative position for a camera that is today a simple link to an As you could see in Section 2.2, the Standard 22311,
image (the plan of the network of cameras or of a building). This defines a fix position of video surveillance camera, through the
kind of location is not easily exploitable. Furthermore, the GPS coordinates and a link to an image containing the plan of the
standard does not consider the video surveillance cameras’ network. In order to overcome this issue, we propose to compute a
mobility. In order to overcome these issues, we propose to extend relative position with regards to a map which will enable us to:
this standard through a multilayer modelling approach, where the Model the distances between the cameras and select the
network of cameras is put on top of a transportation network. relevant cameras for a certain trajectory;
In the following, we present a state of the art of the mobile Model the connections between the cameras ( e.g., possible
objects modelling as the cameras’ mobility management path between camera1 and camera2 but not between
represents the main focus of this paper. camera2 and camera3 );
Model trajectories for mobile cameras;
Model the fields of view and the maximum detection
distances of fixed and mobile cameras.
2.3 Mobile Objects Modelling
With the technology’s evolution, the mobility became very In order to achieve this goal we took our inspiration from the
important in the context of video surveillance systems. Not only domain of GIS (Geographical Information Systems) [4] and
the objects (e.g., persons, cars) are moving in the monitored mobile objects modelling.
scene, but also the surveillance cameras are moving. The great
majority of the research papers concerning the mobile objects in By considering the video surveillance system as a GIS we
the video surveillance domain concentrate on the video content benefit from the separation between the conceptual layers. Thus at
analysis in order to detect and track the objects, to interpret their any time, a new layer can be added without modifying the existing
behaviour and to understand the visual events of the monitored layers.
scene [10]. Thus, the mobility of the cameras is not exploited. In our approach, we propose a four layer model: (1) Road
In the field of moving objects, a mobile object means the network, (2) Transportation network, (3) Objects and (4) Cameras
continuous evolution of any object over the time, in terms of network. The Figure 5 illustrates the UML model for the first
position and dimension [21]. This movement of the mobile three layers.
objects can be effectuated in an unconstrained environment [18] The “Road network” layer, presented in blue in Figure 5, is
(e.g., for hurricanes, fires) or in a constrained environment [17] based on the graph modelling approach well-known in the
(e.g., cars move on road and transportation networks). literature. The road network is considered as an undirected graph
In the video surveillance domain, the objects are moving in a G= (V, E), with V a set of vertices and E a set of edges defined
constrained environment, mainly by the road network. This according to the granularity level that we want to consider (for a
environment is represented as a graph-based model [6], [15], [25], big boulevard of a European capital for example we can consider
where the vertices are junctions and the edges are the roads each segment of the road, each segment between two intersections
between the two junctions. [9] considers also the connectivity at or the entire boulevard). Each vertex has an identifier and a 2D
each junction in order to represent the road network. [19] extends position. Each edge is determined by two vertices.
the model proposed by [9] in order to consider the predefined The “Transportation network”, presented in yellow in
trajectories that some objects could have (e.g., buses). [7] Figure 5, is also based on a graph model. At this level, the vertices
proposes a mobile object data model where they consider the road of the transportation network are intersections between roads, and
and rail networks. [2] takes into account the transport network in bus stations. Each transportation vertex has a position with
a city as a graph and they add to each graph vertex the transport regards to a road segment. Ordered sequences of transportation
modes available (i.e., pedestrian, auto, urban rail, metro, bus). vertices constitute sections, which form lines (e.g., bus lines). The
In the management of mobile objects, a major issue is the advantage of our approach with regards to the ones proposed in
storage of the objects’ spatio-temporal positions. Several the state of the art [9] is that we have two independent graphs that
strategies can be considered: using the spatio-temporal data types are connected to each other through the positions of transportation
vertices. That way if the buses stations are modified or new buses
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Proceedings IMMoA’13 55 http://www.dbis.rwth-aachen.de/IMMoA2013/
lines are introduced we do not have to recompute the underlying On top of all these layers, we model a video surveillance
road graph. cameras’ network. A simplified schema of this model is illustrated
in Figure 6.
The “Objects” layer, presented in red in Figure 5, models
the positions of fixed and mobile objects with regards to the The cameras’ network is composed of fixed and mobile
underlying layers. cameras. The fixed cameras have a 2D position that is given at
installation time. The mobile cameras are associated with mobile
The Fixed Object has a position on a road segment. Its
objects (e.g., buses) and their trajectory is the same as the object’s
position is defined as a distance from each end of the segment.
one.
For this kind of objects, we adopt the same localisation as the one
proposed by [9]. The new generation of digital surveillance cameras has
embedded GPS transmitters and even compasses. The
In the case of Mobile Objects (e.g., buses, police cars,
technologies developed around these cameras make it possible to
persons), the position changes in time. Each object will
automatically extract information from the camera related to its
periodically transmit its position using different strategies (e.g.,
orientation, pan, tilt, zoom, focal distance, compression
each Δt seconds, each time the object is changing the segment,
parameters etc.
when the object's position predicted by the motion vector deviates
from the real position by more than a threshold [23]) that are out Based on all these elements it is possible to model the field
of the scope of this article. We suppose that we periodically of view for each camera and track its modifications in time. The
receive updates containing time-stamped GPS points that we field of view is computed based on four parameters [1]: the 2D
transform into a relative position with regards to the road network position, the viewable angle, the orientation and the visible
(i.e., the segments). We use this information to reconstitute distance. A schema of a 2D field of view proposed by [1] is
object’s trajectory. shown in Figure 7.
We distinguish two types of mobile objects: objects that
move freely within the road and transportation networks (e.g., car,
person) and objects of which trajectories are constrained by a
“line” (e.g., buses).
Figure 6: "Cameras network" layer
P : camera location ()
θ : viewable angle
d : camera direction vector
R: visible distance
Figure 7: Illustration of the field of view model in 2D [1]
Figure 5: "Road network", "Transportation network" and
"Objects" layers
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Figure 8: General architecture of the system
In order to select the most appropriate attributes to describe More precisely, the idea is to compare a spatio-temporal
a video surveillance camera, we studied the sensor description query of the user (e.g., Rivoli Street from Louvre to Metro
proposed by the ISO 22311 standard, SensorGML6, KML7. We Chatelet the 14th of July between 10h and 14h) with the
separated the identified camera’s properties in two categories: trajectories stored in our database and, for a better precision, with
properties that could be modified over the time, and fixed the cameras fields of view. The Figure 8 illustrates the generic
characteristics. architecture of a system based on our spatio-temporal database for
assisting the video surveillance in their research.
Thus, the extension of the standard ISO 22311 is realised at
three levels: From the Figure 8 it is easy to observe that there are two
main questions when developing such system: How to query the
Taking into account the road and transportation networks as system? and How to update the system?. As explained in the
a graph and not as an image; previous section our work addresses only the querying aspect that
Taking into account the camera’s relative position and its we are going to describe in the following.
mobility on the networks;
First, a Query Interpreter module will transform the user
Taking into account the camera’s characteristics change
query (e.g, Rivoli Street from Louvre to Metro Chatelet the 14 th of
over the time.
July between 10h and 14h) in a spatio-temporal query. By spatio-
Our model is implemented in a spatio-temporal database temporal query we understand a sequence of road segments and a
that can be queried by users in order to retrieve the relevant time interval that will be further transformed in a SQL query, by
cameras for a given trajectory. The originality of our research the SQL Query Generator module. The SQL query is executed on
work is given by: the database having as a result a list of cameras. Based on some
image quality parameters a score per camera can be computed and
the fact that it combines different spatio-temporal the initial list can then be ranked according to this relevance
information (e.g., road network, transportation network, score.
objects’ positions) and computation (e.g., trajectories, field
of view) within the same database; In the following we present two examples of spatio-
temporal queries executed on our database implemented in Oracle
the twofold mobility, of the target objects and of the
Spatial 8:
cameras.
In the next section we present the general architecture of the The first selects the fixed cameras of which geometry
tool that could assist the video surveillance operators in their (field of view) intersects the geometry of the Rivoli street;
research based on our spatio-temporal database and some SELECT IdCamera
examples of queries. FROM FixedCamera
WHERE SDO_RELATE(
camera_geom,
(SELECT street_geom
4. Spatiotemporal database and queries FROM Road
Based on the presented model, our goal is to automatically
WHERE Name ='Rivoli‘ ),
select the cameras (fixed and mobile) that could contain relevant
'mask=OVERLAPBDYDISJOINT querytype=WINDOW'
video content with regards to the user query (their field of view
)='TRUE';
intersected the query trajectory).
6
http://www.opengeospatial.org/standards/sensorml 8
http://www.oracle.com/fr/products/database/options/spatial/index
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http://www.opengeospatial.org/standards/kml .html
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The second selects the mobile cameras that are associated Another perspective of our work is the improvement of the
with the buses that crossed the street within the given time resulted cameras list by re-ranking it based on cameras’
interval. characteristics (e.g., image quality, visible distance).
LET TimePeriod = Timestamp(hour(2013,1,14,10),
hour(2013,1,14, 12));
SELECT ObjetID 6. ACKNOWLEDGMENTS
FROM ConstrainedObject This work has been supported by the ANR CSOSG-National
WHERE Type.MobileObject= “Bus” AND Security (French National Research Agency) project
TimePeriod.ConstrainedObject (atperiods (Timestamp, METHODEO.
TimePeriod));
SELECT DISTINCT IdMobileCamera
FROM ConstrainedObject, FreeObject, MobileCamera
7. REFERENCES
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