AMUSE
The automotive multi-sensor (AMUSE) dataset consists of inertial and other complementary sensor data combined with monocular, omnidirectional, high frame rate visual data taken in real traffic scenes during multiple test drives.
Quicklinks:
DATASET DESCRIPTION
A short description of the dataset can be found in the paper published on CVPR 2013 workshop "Ground Truth - What is a good dataset?" . The paper can also be accessed here .
Please cite us, when using this dataset.
author = {Philipp Koschorrek and Tommaso Piccini and Per Öberg and
Michael Felsberg and Lars Nielsen and Rudolf Mester},title = {A multi-sensor traffic scene dataset with omnidirectional video},
booktitle = {Ground Truth - What is a good dataset? CVPR Workshop 2013},
year = {2013}
}
The table below shows the actual list of recorded sequences. Sequences marked with ^ are not distributed via web. Only sample images are available. Further information about how to get these sequences can be found in the distribution section . Sequences marked with * can be downloaded fully. Those sequences are short ones for demonstration purpose. Therefore the driven way is minor and not mentioned.
Paper name |
Sequence
name |
Length
[km] |
#Frames |
Size [GB]
|
Specifics |
---|---|---|---|---|---|
Seq1 |
20130227_CVL_0_
AroundLHouse^ |
0.7 | 4180 | 45 |
Loop closing, (nearly) static scene, partly snow
|
Seq2 |
20130227_CVL_1_
ToLambohov^ |
2.9 | 9826 | 106 |
real traffic, partly snow
|
Seq3
|
20130227_CVL_2_
RoundsInLambohov^ |
1.4 | 6689 | 72 | Loop closing, suburban area, little traffic, low altitude of sun |
Seq4
|
20130227_CVL_3_
GarnisonenToValla^ |
4.6 | 21912 | 236 | Urban area, traffic, low altitude of sun |
Seq5
|
20130319_CVL_0_
OnValla^ |
1.4 | 6469 | 70 |
Snow
|
Seq6 |
20130319_CVL_1_
VallaToTornby^ |
4.3 | 19844 | 210 |
Snow, urban area, traffic, water and snow on lens
|
Seq7 |
20130319_CVL_2_
TornbyToValla^ |
9.1 | 39830 | 430 |
Snow, urban area, traffic, water and snow on lens
|
- |
20130530_CVL_0_
DrivingARound * |
- | 720 | 8 |
Short sequence
|
-
|
20130530_CVL_1_
StraightForward * |
- | 522 | 6 | Short sequence |
- |
20130530_CVL_2_
Turn * |
- | 674 | 7 |
Short sequence
|
- |
20130530_CVL_3_
BackwardForward * |
-
|
631 |
7
|
Short sequence
|
- |
20130530_CVL_4_
Braking * |
- | 317 |
4
|
Short sequence
|
- |
20130530_CVL_5_
Parking * |
-
|
680 | 7 |
Short sequence
|
- |
20130530_CVL_6_
AroundRoundabout^ |
2.1 | 5146 | 55 |
Short sequence, real traffic
|
Due to the high frame rate and resolution of the cameras, the number of the cameras and the lossless raw image storage one whole sequence can easily reach several Gigabytes. Therefore a download of the sequences is hardly possible.
We offer instead to send the whole set of sequences on an external hard drive disk by a price of 4000 Swedish Krona(SEK) incl. worldwide shipping (as of 2013-06-23, this price may change). Feel free to contact us .
Note: Shipping starts as soon as possible. Information about shipping can be found here.
For reading the sensor data stored in streamfiles an API is needed, the StreamReader . There are different APIs for different programming languages. The specific APIs can downloaded by clicking on the corresponding programming language. Documentation of the functions is inline. A common description of the functions can be found in the C/C++-documentation .
ROS
For initialization the API needs the paths to the project on the one hand and to a XML file on the other hand which describes the IDs and messages used in the streamfiles. These paths can be committed directly or stored in a XML file which is used instead (more information in the API documentation). Below are the mandatory message/ID XML file and an example file of the initialization XML file.
Furthermore, information about the experimental setup, the stream format, etc. can be found in the links below.
Sketches of the experimental setup
Detailed sensor description incl. camera calibration parameter
Stream format description
GUIDELINES FOR EXPANDING OUR WORK
Information to be published soon.
AMUSE dataset by
CVL, Linköping University,
is licensed under a
Creative Commons
Attribution-NonCommercial-NoDerivs 3.0 Unported License
.
|
Last updated: 2014-05-12