DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition

Yuhuang Hu, Hongjie Liu, Michael Pfeiffer and Tobi Delbruck

Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zurich, Switzerland



Quick Fact

Four targeted frame-based datasets
VOT 2015 Dataset Tracking Dataset UCF-50 Dataset Caltech-256 Dataset
Single Target Object Tracking Single Target Object Tracking Action Recognition Object Recognition
Statistics of converted DVS datasets
Name Domain Nr. Recordings Avg. Length/recording (s) Max. FR (keps) Avg. FR (keps)
VOT 2015 Tracking 60 12.25 383.63 251.85
Tracking Dataset Tracking 67 20.70 342.07 197.77
UCF-50 Action Recognition 6676 6.80 238.11 162.62
Caltech-256 Object Recognition 30607 1.01 N/A 110.57

How to Get Datasets


Through Resillio Sync (RECOMMENDED)

All datasets can be downloaded through the personal file sharing service BitTorrent Sync. Use this link to access the datasets.

Direct Download

__Download instructions coming up!__


Software

We developed a Python package called SpikeFuel that accompanies jAER for

  • Precise control of record logging with Python.
  • User interface for showing video or images in routine.
  • Experiment configuration system (with JSON style).
  • Post signal analysis and selection tools.

A Closer Look



Contacts

Questions about these datasets should be directed to:

References

Acknowledgments

This research is supported by the European Commission project VISUALISE (FP7-ICT-600954), SeeBetter (FP7-ICT-270324), and the Samsung Advanced Institute of Technology.

We gratefully acknowledge the creators of the original datasets.


This dataset is hosted as part of the INI Sensors Group Databases