2024年2月25日发(作者：喻永福)
PETS 2007 基准数据集-数据集 S1:普通的游荡1(PETS 2007 Benchmark Data-Dataset S1:general
loitering 1)
数据介绍：
The datasets are multisensor sequences containing the following 3
scenarios, with increasing scene complexity: 1. loitering, 2. attended
luggage removal (theft), 3. unattended luggage. The results of processing
the datasets are to be submitted in XML format (details below).
关键词：
PETS 2007,基准,游荡,盗窃,无人看管的行李,多传感器序列, PETS
2007,Benchmark,loitering,theft,unattended luggage,multisensor
sequences,
数据格式：
VIDEO
数据详细介绍：
PETS 2007 Benchmark Data-Dataset S1:general loitering 1
Overview
The datasets are multisensor sequences containing the following 3 scenarios,
with increasing scene complexity: 1. loitering, 2. attended luggage removal
(theft), 3. unattended luggage. The results of processing the datasets are to be
submitted in XML format (details below).
Please e-mail datasets@ if you require assistance obtaining
these datasets for the workshop.
Aims and Objectives
The aim of this workshop is to employ existing (or new) systems for the
detection of one or more of 3 types of security/criminal events, within a
real-world environment. The scenarios are filmed from multiple cameras and
involve multiple actors.
Preliminaries
Please read (and re-read) the following information carefully before processing
the dataset, as the details are essential to the understanding of when
warning/alarm events should be generated by your system. In particular,
please observe the spatial and temporal requirements for each scenario. For
the purposes of all scenarios, "entering the scene" refers to a person or
persons
entering the field of view of camera 3 for the first time.
1. Definition of Loitering
Loitering is defined as a person who enters the scene, and remains within the
scene for more than t seconds. For the purposes of PETS 2007, t = 60
seconds.
2. Definition of Left-Luggage
Left-luggage in the context of PETS 2007 is defined as items of luggage that
have been abandoned by their owner. It is based on the definition used for
PETS 2006
To implement a system based on this definition there are three additional
components that need to be defined:
items are classed as luggage? Luggage is defined to include all types
of baggage that can be carried by trunks, bags, rucksacks,
backpacks, parcels, and suitcases.
Four common types of luggage are considered in this study:
1. Handbag
2. Carry-on case
3. 70 litre backpack
4. Ski gear carrier
constitutes attended and unattended luggage? In this study three rules
are used to determine whether luggage is attended to by a person (or not):
1. A luggage is owned and attended to by a person or persons who enter
the scene with the luggage until such point that the luggage is not in
physical contact with the person (contextual rule).
2. At this point the luggage is attended to by the owner ONLY when they
are within a distance
a metres of the luggage (spatial rule). All distances
are measured between object centroids on the ground plane (i.e. z=0).
If a person is within
a (=2) metres of their luggage no alarm should be
raised by the system.
3. A luggage item is unattended when the owner is further than
b metres
(where
b>=a *) from the luggage. If a person crosses the line at
b(=3)
metres the system should use the spatio-temporal rule in item C, below,
to detect whether this item of luggage has been abandoned (an alarm
event).
* If
b >
a, the distance between radii
a and
b is determined to be a warning
zone where the luggage is neither attended to nor left unattended. This zone is
defined to separate the detection points of the two states, reducing
uncertainties introduced due to calibration / detection errors in the sensor
system etc. If a person crosses the line at
a (=2) metres, but within the
radius
b (=3) metres, the system can be set up to trigger a warning event,
using a rule similar to the spatio-temporal rule in item C, below. Both warning
and alarm events will be given in the ground truth.
C. What constitutes abandonment of luggage by the owner? The
abandonment of an item of luggage is defined spatially and temporally.
Abandonment (causing an alarm) is defined as:
1. An item of luggage that has been left unattended by the owner for a
period of
t (=25) consecutive seconds in which time the owner has not
re-attended to the luggage, nor has the luggage been attended to by a
second party (instigated by physical contact, in which case a theft /
tampering event may be raised). If an item of luggage is left unattended
for
t (=25) seconds, the alarm event is triggered.
3. Definition of Attended Luggage Removal (Theft)
The theft of an item of luggage is defined using a spatial constraint only. Theft
is defined as an item of luggage moved further than
b (=3) metres away from
the owner. A warning can be issued at
a (=2) metres away from the owner.
Calibration Data
Equidistant markers placed on the floor of the terminal were used for
calibration purposes. The following point locations were used as the calibration
points used (click to view full resolution image):
The image contains an example of the "real-world co-ordinate system" the
cameras convert to EXCEPT that the green units correspond to one "square
with black crosses at corners" distance, which in real world units is 1.8m (to a
tolerance of +- 1cm). (Each black cross square is composed by 3x3 floor tiles
each of which is 0.6m by 0.6m.) The (0,0) point is both the origin of the
calibration and the point at which bags are put down on the ground . The "pixel
positions" take the top left corner as (0,0) with travelling horizontally right
increasing the first coordinate and travelling vertically down increasing the
second coordinate (ie, the xv convention).
All spatial measurements are in metres. The provided calibration parameters
were obtained using the freely available Tsai Camera Calibration
Software (/afs//user/rgw/www/)by Reg Willson. For instructions on how to use Reg Willsons software visit
Chris Needhams
helpful page(/chrisn/Tsai/). More
information on the Tsai camera model is available
on CVonline(/rbf/CVonline/LOCAL_COPIES/DIAS1/).
An example of the provided calibration parameter XML file is
given here(). This XML file contains Tsai camera parameters
obtained from Reg Willsons software, using the calibration points image shown
above and this set of points(pets2007_). C++ code
(available here(sample)) is provided to allow you to load and use the
calibration parameters in your program (courtesy of
project ETISEO(/etiseo/)). Please note that separate
calibration parameters are provided for each Scenario (located within each
Scenario .zip)
The DV cameras used to film all datasets are:
Camera 1: Canon MV-1 1xCCD w/progressive scan
Camera 2: Sony DCR-PC1000E 3xCMOS
Camera 3: Canon MV-1 1xCCD w/progressive scan
Camera 4: Sony DCR-PC1000E 3xCMOS
The resolution of all sequences are PAL standard (full colour, 768 x 576 pixels,
25 frames per second) and compressed as JPEG image sequences (approx.
90% quality).
XML Schema
All scenarios come with four XML files. The XML files contains the camera
calibration parameters for camera views 1-4 respectively.
The XML schema for the configuration / submission is
given here().
For submitted XML not all details need to be provided. An example of the
(minimum) data to be submitted is given here().
Training Data
Background images are provided of the monitored surveillance system. Note
that the scene is never completely empty of people. However, it is envisaged
that the data is useful for training some systems. Note that testing (and
corresponding generation of results) should not be performed on the training
data, only based on some or all of sequences S0-S8 below.
Download
The background training data (including the calibration data)
(1000 frames, 40s, 241 Mb)
Dataset S0
Scenario: nothing happening
Elements: no actors, no bags, medium density crowd
Ground truth parameters: N/A
Subjective Difficulty:
This scenario is a control sequence in which none of the events defined
(loitering, theft, unattended luggage) takes place.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (4500 frames, 180s,
862 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S1
Scenario: general loitering 1
Elements: 1 actor, no bags, medium crowd
Ground truth parameters: t = 60 seconds
Subjective Difficulty:
This scenario contains one person who enters the scene and then loiters,
remaining almost motionless at times, then leaves the scene.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (4001 frames,
160.04s, 862 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S2
Scenario: general loitering 2
Elements: 1 actor, 1 large bag, medium density crowd
Ground truth parameters: t = 60 seconds
Subjective Difficulty:
This scenario contains a person who walks into the scene carrying a bag which
they then proceed to put down on the ground. The person then loiters in the
middle of the scene before exiting.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (4500 frames, 180s,
839 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S3
Scenario: theft 1
Elements: 2 actors, 1 small bag, low density crowd
Ground truth parameters: N/A
Subjective Difficulty:
This scenario contains two persons who enter the scene, one carrying a
shoulder bag. Both persons walk to the centre of the scene before the bag
owner places the bag on the ground. The second person picks up the bag and
both persons then proceed to walk out of the scene. No warning/alarm should
be generated as the bag remains within a/b metres of the owner at all times.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (2971 frames,
118.84s, 693 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S4
Scenario: theft 2
Elements: 4 actors, 1 large bag, low density crowd
Ground truth parameters: N/A
Subjective Difficulty:
This scenario contains four persons who walk into the scene, one carrying a
rucksack. One of the other persons picks up the bag and all walk out of the
scene. No warning/alarm should be generated as the rucksack remains within
a/b metres of the owner at all times.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (3500 frames, 140s,
789 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S5
Scenario: theft 3
Elements: 2 actors, 1 large bag, medium density crowd
Ground truth parameters: a = 2 metres, b = 3 metres
Subjective Difficulty:
This scenario contains one person who enters the scene carrying a large
rucksack, which is placed on the ground. A second person (thief) picks up the
bag and walks out of the scene, without the bag owner immediately noticing.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (2900 frames, 116s,
639 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S6
Scenario: theft 4
Elements: 4 actors, 2 large bags, medium density crowd
Ground truth parameters: a = 2 metres, b = 3 metres
Subjective Difficulty:
This scenario contains two persons who enter the scene carrying two large
bags. They place the bags down on the ground, to give directions a passer by
(third person). While the bag owner is distracted, a fourth person (thief) picks
up and walks away with one of the bags, without the owner immediately
noticing.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenari including the calibration data (2735 frames, 109.4s,
577 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S7
Scenario: left luggage 1
Elements: 1 actor, 1 small & 1 large bag, low density crowd
Ground truth parameters: a = 2 metres, b = 3 metres, t = 25 seconds
Subjective Difficulty:
This scenario contains a single person with two bags. The individual enters the
scene, stops in the middle of the scene, before walking away whilst
accidentally leaving one bag on the ground. The bag owner then returns to the
scene to retrieve the bag.
Sample Images
The following images show representative images captured from cameras
1-4 .
Download
The entire scenario including the calibration data (3000 frames, 120s,
708 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S8
Scenario: left luggage 2
Elements: 1 actor, 1 large bag, low desnity crowd
Ground truth parameters: a = 2 metres, b = 3 metres, t = 25 seconds
Subjective Difficulty:
This scenario contains an individual who enters the scene carrying a large bag,
which is placed on the ground. The owner then walks away from the bag
before retrieving it, and leaving the scene.
Sample Images
The following images show representative images captured from cameras
1-4 .
Download
The entire scenari including the calibration data (3000 frames, 120s,
646 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Additional Information
Legal note: The UK Information Commisioner has agreed that the PETS 2007
datasets described here may be made publicly available for the purposes of
academic research. The video sequences are copyright UK EPSRC REASON
Project consortium and permission is hereby granted for free download for the
purposes of the PETS 2007 workshop.
数据预览：
点此下载完整数据集
2024年2月25日发(作者：喻永福)
PETS 2007 基准数据集-数据集 S1:普通的游荡1(PETS 2007 Benchmark Data-Dataset S1:general
loitering 1)
数据介绍：
The datasets are multisensor sequences containing the following 3
scenarios, with increasing scene complexity: 1. loitering, 2. attended
luggage removal (theft), 3. unattended luggage. The results of processing
the datasets are to be submitted in XML format (details below).
关键词：
PETS 2007,基准,游荡,盗窃,无人看管的行李,多传感器序列, PETS
2007,Benchmark,loitering,theft,unattended luggage,multisensor
sequences,
数据格式：
VIDEO
数据详细介绍：
PETS 2007 Benchmark Data-Dataset S1:general loitering 1
Overview
The datasets are multisensor sequences containing the following 3 scenarios,
with increasing scene complexity: 1. loitering, 2. attended luggage removal
(theft), 3. unattended luggage. The results of processing the datasets are to be
submitted in XML format (details below).
Please e-mail datasets@ if you require assistance obtaining
these datasets for the workshop.
Aims and Objectives
The aim of this workshop is to employ existing (or new) systems for the
detection of one or more of 3 types of security/criminal events, within a
real-world environment. The scenarios are filmed from multiple cameras and
involve multiple actors.
Preliminaries
Please read (and re-read) the following information carefully before processing
the dataset, as the details are essential to the understanding of when
warning/alarm events should be generated by your system. In particular,
please observe the spatial and temporal requirements for each scenario. For
the purposes of all scenarios, "entering the scene" refers to a person or
persons
entering the field of view of camera 3 for the first time.
1. Definition of Loitering
Loitering is defined as a person who enters the scene, and remains within the
scene for more than t seconds. For the purposes of PETS 2007, t = 60
seconds.
2. Definition of Left-Luggage
Left-luggage in the context of PETS 2007 is defined as items of luggage that
have been abandoned by their owner. It is based on the definition used for
PETS 2006
To implement a system based on this definition there are three additional
components that need to be defined:
items are classed as luggage? Luggage is defined to include all types
of baggage that can be carried by trunks, bags, rucksacks,
backpacks, parcels, and suitcases.
Four common types of luggage are considered in this study:
1. Handbag
2. Carry-on case
3. 70 litre backpack
4. Ski gear carrier
constitutes attended and unattended luggage? In this study three rules
are used to determine whether luggage is attended to by a person (or not):
1. A luggage is owned and attended to by a person or persons who enter
the scene with the luggage until such point that the luggage is not in
physical contact with the person (contextual rule).
2. At this point the luggage is attended to by the owner ONLY when they
are within a distance
a metres of the luggage (spatial rule). All distances
are measured between object centroids on the ground plane (i.e. z=0).
If a person is within
a (=2) metres of their luggage no alarm should be
raised by the system.
3. A luggage item is unattended when the owner is further than
b metres
(where
b>=a *) from the luggage. If a person crosses the line at
b(=3)
metres the system should use the spatio-temporal rule in item C, below,
to detect whether this item of luggage has been abandoned (an alarm
event).
* If
b >
a, the distance between radii
a and
b is determined to be a warning
zone where the luggage is neither attended to nor left unattended. This zone is
defined to separate the detection points of the two states, reducing
uncertainties introduced due to calibration / detection errors in the sensor
system etc. If a person crosses the line at
a (=2) metres, but within the
radius
b (=3) metres, the system can be set up to trigger a warning event,
using a rule similar to the spatio-temporal rule in item C, below. Both warning
and alarm events will be given in the ground truth.
C. What constitutes abandonment of luggage by the owner? The
abandonment of an item of luggage is defined spatially and temporally.
Abandonment (causing an alarm) is defined as:
1. An item of luggage that has been left unattended by the owner for a
period of
t (=25) consecutive seconds in which time the owner has not
re-attended to the luggage, nor has the luggage been attended to by a
second party (instigated by physical contact, in which case a theft /
tampering event may be raised). If an item of luggage is left unattended
for
t (=25) seconds, the alarm event is triggered.
3. Definition of Attended Luggage Removal (Theft)
The theft of an item of luggage is defined using a spatial constraint only. Theft
is defined as an item of luggage moved further than
b (=3) metres away from
the owner. A warning can be issued at
a (=2) metres away from the owner.
Calibration Data
Equidistant markers placed on the floor of the terminal were used for
calibration purposes. The following point locations were used as the calibration
points used (click to view full resolution image):
The image contains an example of the "real-world co-ordinate system" the
cameras convert to EXCEPT that the green units correspond to one "square
with black crosses at corners" distance, which in real world units is 1.8m (to a
tolerance of +- 1cm). (Each black cross square is composed by 3x3 floor tiles
each of which is 0.6m by 0.6m.) The (0,0) point is both the origin of the
calibration and the point at which bags are put down on the ground . The "pixel
positions" take the top left corner as (0,0) with travelling horizontally right
increasing the first coordinate and travelling vertically down increasing the
second coordinate (ie, the xv convention).
All spatial measurements are in metres. The provided calibration parameters
were obtained using the freely available Tsai Camera Calibration
Software (/afs//user/rgw/www/)by Reg Willson. For instructions on how to use Reg Willsons software visit
Chris Needhams
helpful page(/chrisn/Tsai/). More
information on the Tsai camera model is available
on CVonline(/rbf/CVonline/LOCAL_COPIES/DIAS1/).
An example of the provided calibration parameter XML file is
given here(). This XML file contains Tsai camera parameters
obtained from Reg Willsons software, using the calibration points image shown
above and this set of points(pets2007_). C++ code
(available here(sample)) is provided to allow you to load and use the
calibration parameters in your program (courtesy of
project ETISEO(/etiseo/)). Please note that separate
calibration parameters are provided for each Scenario (located within each
Scenario .zip)
The DV cameras used to film all datasets are:
Camera 1: Canon MV-1 1xCCD w/progressive scan
Camera 2: Sony DCR-PC1000E 3xCMOS
Camera 3: Canon MV-1 1xCCD w/progressive scan
Camera 4: Sony DCR-PC1000E 3xCMOS
The resolution of all sequences are PAL standard (full colour, 768 x 576 pixels,
25 frames per second) and compressed as JPEG image sequences (approx.
90% quality).
XML Schema
All scenarios come with four XML files. The XML files contains the camera
calibration parameters for camera views 1-4 respectively.
The XML schema for the configuration / submission is
given here().
For submitted XML not all details need to be provided. An example of the
(minimum) data to be submitted is given here().
Training Data
Background images are provided of the monitored surveillance system. Note
that the scene is never completely empty of people. However, it is envisaged
that the data is useful for training some systems. Note that testing (and
corresponding generation of results) should not be performed on the training
data, only based on some or all of sequences S0-S8 below.
Download
The background training data (including the calibration data)
(1000 frames, 40s, 241 Mb)
Dataset S0
Scenario: nothing happening
Elements: no actors, no bags, medium density crowd
Ground truth parameters: N/A
Subjective Difficulty:
This scenario is a control sequence in which none of the events defined
(loitering, theft, unattended luggage) takes place.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (4500 frames, 180s,
862 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S1
Scenario: general loitering 1
Elements: 1 actor, no bags, medium crowd
Ground truth parameters: t = 60 seconds
Subjective Difficulty:
This scenario contains one person who enters the scene and then loiters,
remaining almost motionless at times, then leaves the scene.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (4001 frames,
160.04s, 862 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S2
Scenario: general loitering 2
Elements: 1 actor, 1 large bag, medium density crowd
Ground truth parameters: t = 60 seconds
Subjective Difficulty:
This scenario contains a person who walks into the scene carrying a bag which
they then proceed to put down on the ground. The person then loiters in the
middle of the scene before exiting.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (4500 frames, 180s,
839 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S3
Scenario: theft 1
Elements: 2 actors, 1 small bag, low density crowd
Ground truth parameters: N/A
Subjective Difficulty:
This scenario contains two persons who enter the scene, one carrying a
shoulder bag. Both persons walk to the centre of the scene before the bag
owner places the bag on the ground. The second person picks up the bag and
both persons then proceed to walk out of the scene. No warning/alarm should
be generated as the bag remains within a/b metres of the owner at all times.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (2971 frames,
118.84s, 693 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S4
Scenario: theft 2
Elements: 4 actors, 1 large bag, low density crowd
Ground truth parameters: N/A
Subjective Difficulty:
This scenario contains four persons who walk into the scene, one carrying a
rucksack. One of the other persons picks up the bag and all walk out of the
scene. No warning/alarm should be generated as the rucksack remains within
a/b metres of the owner at all times.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (3500 frames, 140s,
789 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S5
Scenario: theft 3
Elements: 2 actors, 1 large bag, medium density crowd
Ground truth parameters: a = 2 metres, b = 3 metres
Subjective Difficulty:
This scenario contains one person who enters the scene carrying a large
rucksack, which is placed on the ground. A second person (thief) picks up the
bag and walks out of the scene, without the bag owner immediately noticing.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenario including the calibration data (2900 frames, 116s,
639 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S6
Scenario: theft 4
Elements: 4 actors, 2 large bags, medium density crowd
Ground truth parameters: a = 2 metres, b = 3 metres
Subjective Difficulty:
This scenario contains two persons who enter the scene carrying two large
bags. They place the bags down on the ground, to give directions a passer by
(third person). While the bag owner is distracted, a fourth person (thief) picks
up and walks away with one of the bags, without the owner immediately
noticing.
Sample Images
The following images show representative images captured from cameras 1-4.
Download
The entire scenari including the calibration data (2735 frames, 109.4s,
577 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S7
Scenario: left luggage 1
Elements: 1 actor, 1 small & 1 large bag, low density crowd
Ground truth parameters: a = 2 metres, b = 3 metres, t = 25 seconds
Subjective Difficulty:
This scenario contains a single person with two bags. The individual enters the
scene, stops in the middle of the scene, before walking away whilst
accidentally leaving one bag on the ground. The bag owner then returns to the
scene to retrieve the bag.
Sample Images
The following images show representative images captured from cameras
1-4 .
Download
The entire scenario including the calibration data (3000 frames, 120s,
708 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Dataset S8
Scenario: left luggage 2
Elements: 1 actor, 1 large bag, low desnity crowd
Ground truth parameters: a = 2 metres, b = 3 metres, t = 25 seconds
Subjective Difficulty:
This scenario contains an individual who enters the scene carrying a large bag,
which is placed on the ground. The owner then walks away from the bag
before retrieving it, and leaving the scene.
Sample Images
The following images show representative images captured from cameras
1-4 .
Download
The entire scenari including the calibration data (3000 frames, 120s,
646 Mb)
The ground truth for this scenario is available here. Please check regularly for
updates.
Additional Information
Legal note: The UK Information Commisioner has agreed that the PETS 2007
datasets described here may be made publicly available for the purposes of
academic research. The video sequences are copyright UK EPSRC REASON
Project consortium and permission is hereby granted for free download for the
purposes of the PETS 2007 workshop.
数据预览：
点此下载完整数据集