Please read the following information carefully before processing the dataset, as the details are essential to the understanding of when notification of events should be generated by your system. Please check regularly for updates.
The calibration data (one file for each of the 8 cameras) can be found here. The ground plane is assumed to be the Z=0 plane. C++ code (available here) is provided to allow you to load and use the calibration parameters in your program (courtesy of project ETISEO). The provided calibration parameters were obtained using the freely available Tsai Camera Calibration Software by Reg Willson. All spatial measurements are in metres.
The cameras used to film the datasets are:
| view |
Model |
Resolution |
frame
rate |
Comments |
| 001 |
Axis
223M |
768x576 |
~7 |
Progressive
scan |
| 002 |
Axis 223M | 768x576 | ~7 |
Progressive scan |
| 003 |
PTZ
Axis 233D |
768x576 | ~7 |
Progressive scan |
| 004 |
PTZ Axis 233D | 768x576 |
~7 |
Progressive scan |
| 005 |
Sony DCR-PC1000E 3xCMOS | 720x576 |
~7 |
ffmpeg
De-interlaced |
| 006 |
Sony DCR-PC1000E 3xCMOS | 720x576 | ~7 |
ffmpeg De-interlaced |
| 007 |
Canon MV-1 1xCCD w | 720x576 | ~7 |
Progressive scan |
| 008 |
Canon MV-1 1xCCD w | 720x576 | ~7 |
Progressive scan |
The direct link to the Google maps is as follows: Google Maps
| view 001 | view 002 | view 003 | view 004 |
 |
 |
 |
 |
| view 005 | view 006 | view 007 | view 008 |
 |
 |
 |
 |
Please note that while effort has been
made to make sure the frames from different views are synchronised,
there might be slight delays and frame drops in some cases. In particular View 4 suffers from frame
rate instability and we suggest it be used as a supplementary source of
information. Please let us know if you encounter any problems or
inconsistencies.
Three regions, R0, R1 and R2 are
defined in View 001 only (shown in the example image). The coordinates of
the
top left and bottom right corners (in pixels) are given in the
following table.
Definition of crowd density (%): crowd density is based on a maximum occupancy (100%) of 40 people in 10 square metres on the ground. One person is assumed to occupy 0.25 square metres on the ground. |
 |
| Elements:
medium density crowd, overcast Sequences: Sequence 1 with timestamp 13-57; Sequence 2 with timestamp 13-59. Sequences 1-2 use Views 001-004. Subjective Difficulty: Level 1 Task: The task is to count the number of people in R0 for each frame of the sequence in View 1 only. As a secondary challenge the crowd density in regions R1 and R2 can also be reported (mapped to ground plane occupancy, possibly using multiple views). Download
[502 MB]
|
| Elements:
high
density crowd, overcast Sequences: Sequence 1 with timestamp 14-06; Sequence 2 with timestamp 14-31. Sequences 1-2 use Views 001-004. Subjective Difficulty: Level 2 Task: This scenario contains a densely grouped crowd who walk from one point to another. There are two sequences corresponding timestamps 14-06 and 14-31. The task related to timestamp 14-06 is to estimate the crowd density in Region R1 and R2 at each frame of the sequence. The designated task for the sequence Time_14-31 is to determine both the total number of people entering through the brown line from the left side AND the total number of people exiting from purple and red lines, shown in the opposite figure, throughout the whole sequence. The coordinates of the entry and exit lines are given below for reference.
Download [367 MB] |
 |
| Elements:
medium
density crowd, bright sunshine and shadows Sequences: Sequence 1 with timestamp 14-17; Sequence 2 with timestamp 14-33. Sequences 1-2 use Views 001-004. Subjective Difficulty: Level 3 Task: This scenario contains a crowd of people who, on reaching a point in the scene, begin to run. The task is to measure the crowd density in Region R1 at each frame of the sequence. Download [476 MB] |
| Elements:
sparse crowd Sequences: Sequence 1 with timestamp 12-34 using Views 001-008 except View_002 for cross validation (see below). Subjective Difficulty: L1 Task: Track all of the individuals in the sequence. If you undertake monocular tracking only, report the 2D bounding box location for each individual in the view used; if two or more views are processed, report the 2D bounding box location for each individual as back projected into View_002 using the camera calibration parameters provided (this equates to a leave-one-out validation). Note the origin (0,0) of the image is assumed top-left. Validation will be performed using manually labelled ground truth. Download [997 MB] |
| Elements:
medium
density crowd Sequences: Sequence 1 with timestamp 14-55 using Views 001-004. Subjective Difficulty: L2 Task: Track the individuals marked A and B (see figure) in the sequence and provide 2D bounding box locations of the individuals in View_002 which will be validated using manually labelled ground truth. Note the origin (0,0) of the image is assumed top-left. Note that individual B exits the field of view and returns toward the end of the sequence. Download [442 MB] |
| Elements:
dense crowd Sequences: Sequence 1 with timestamp 14-41 using Views 001-004. Subjective Difficulty: L3 Task: Track the individuals marked A and B in the sequence and provide 2D bounding box information in View_002 for each individual which will be validated using manually labelled ground truth. Download [259 MB] |
| Elements:
dense crowd,
running Sequences: Sequences 1-5 with timestamps 12-43 (using Views 1,2,5,6,7,8) , 14-13, 14-37, 14-46 and 14-52. Sequences 2-5 use Views 001-004. Subjective Difficulty: L2 Task: Detect and estimate the multiple flows in the provided sequences, mapped onto the ground plane as a occupancy map flow. Further details of the exact task requirements are contained under Author Instructions. These would be compared with ground truth optical flow of major flows in the sequences on the ground plane. Download [760 MB] |
| Elements:
dense crowd Sequences: Sequences 1-4 with timestamps 14-16, 14-27, 14-31 and 14-33. Sequences 1-4 use Views 001-004. Subjective Difficulty: L3 Task: This dataset contains different crowd activities and the task is to provide a probabilistic estimation of each of the following events: walking, running, evacuation (rapid dispersion), local dispersion, crowd formation and splitting at different time instances. Furthermore, we are interested in systems that can identify the start and end of the events as well as transitions between them. Download [1.2 GB] |
The scenarios can also be downloaded from ftp://ftp.cs.rdg.ac.uk/pub/PETS2009/ (use anonymous login). Warning: ftp://ftp.pets.rdg.ac.uk is not listing files correctly on some ftp clients. If you experience problems you can connect to the http server at http://ftp.cs.rdg.ac.uk/PETS2009/.
Legal note: The video sequences are copyright University of Reading
and permission is hereby granted for free download for the purposes of
the PETS 2009 workshop and academic and industrial research. Where the
data is disseminated (e.g. publications, presentations) the source
should be acknowledged.
