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
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)|
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.
__Download instructions coming up!__
- 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
Questions about these datasets should be directed to:
Hu, Y., Liu, H., Pfeiffer, M., and Delbruck, T. (2016). DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition. Frontiers in Neuromorphic Engineering, 10:(405).
Hu, Y. (2016). Generation of Benchmarks for Visual Recognition with Spiking Neural Networks. NSC Short Project Report. Zürich, Switzerland: University of Zürich and ETH Zürich.
For more technical information, check out this Google Docs at here.
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