The BOSS dataset is a fairly realistic dataset for pose, action
and interaction recognition. It was originally captured as part
of the Eureka's Celtic Initiative "BOSS
: On Board Wireless Secured Video Surveillance BOSS"
project. It consists of video/audio recordings of acted actions
inside a moving train. It used multiple calibrated cameras.
We have added manual annotations to the original dataset
typically for researchers to use as a ground truth to train and
test classifiers to recognise overall poses (e.g. "standing"),
actions (e.g. "walking") and interactions (e.g. "fighting").
The data is provided only for research purposes.
If you publish work using the data provided in this website,
please cite the following paper:
@inproceedings{velastin2017people, title={People Detection and Pose Classification Inside a Moving Train Using Computer Vision}, author={Velastin, Sergio A and G{\'o}mez-Lira, Diego A}, booktitle={International Visual Informatics Conference}, pages={319--330}, year={2017}, organization={Springer} }
BOSS was a project funded by the EUREKA Celtic Initiative from
Oct 2006-June 2009. In particular, it focused on improving train
passenger security through video and audio analysis. It
generated a data corpus consisting of video/audio recordings
with multiple calibrated cameras of acted scenarios inside a
commuter train. As the train is moving, the data has challenging
lighting conditions. As the cameras are mounted on-board, the
data has challenging viewing conditions (including occlusions).
The pictures below illustrate the captured environment, actions
and interactions.
Simple actions are :
The original recordings used 10 calibrated cameras with audio
installed in various places inside the moving train. The
original data corpus only includes global descriptions,
typically of security alarms, of each video/audio sequences e.g.
"Harass". This is what we call here the "Original Dataset", which is an
unaltered copy of the dataset provided by the BOSS project, and
provided here for convenience.
We have augmented the Original Dataset with temporal
localisation of actions and some interactions. That is what we
call here the "Annotations File".
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We are grateful to Mr Gaetan Crevin, a Masters student from
Telecom Physique Strasbourg (Université de Strasbourg, France),
who ground-truthed this dataset as part of his internship at the
Applied Artificial Intelligence Research Group, Universidad
Carlos III de Madrid, Spain. We are also grateful to Mr. Diego
Gomez-Lira, then a student at the Universidad de Santiago de
Chile, who also ground-truthed part of this dataset.