{"data":{"id":"10.4121/8e609238-55d9-4274-b9f8-ab86a538c416","type":"dois","attributes":{"doi":"10.4121/8e609238-55d9-4274-b9f8-ab86a538c416","prefix":"10.4121","suffix":"8e609238-55d9-4274-b9f8-ab86a538c416","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Hesam Araghi","nameType":"Personal","givenName":"Hesam","familyName":"Araghi","nameIdentifiers":[{"nameIdentifier":"0000-0002-4539-4408","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Nergis Tömen","nameType":"Personal","givenName":"Nergis","familyName":"Tömen","nameIdentifiers":[{"nameIdentifier":"0000-0003-3916-1859","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Jan van Gemert","nameType":"Personal","givenName":"Jan","familyName":"van Gemert","nameIdentifiers":[{"nameIdentifier":"0000-0002-6913-0482","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Code and data supporting the paper “Pushing the Boundary of Event Subsampling in Event-Based Video Classification Using CNNs”"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Electrical and Electronic Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0906"},{"subject":"Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"09"},{"subject":"Event cameras"},{"subject":"Event-based video classification"},{"subject":"event subsampling"},{"subject":"event-count accuracy trade-off"},{"subject":"Neuromorphic machine vision"}],"contributors":[{"name":"TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Pattern Recognition \u0026 Bioinformatics, Delft, The Netherlands","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-11","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"Software","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1007/978-3-031-92460-6_17","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"https://github.com/hesamaraghi/pushing-boundaries-event-subsampling","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/8e609238-55d9-4274-b9f8-ab86a538c416.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["data/.hdf5, script/.py / .sh / .yaml"],"version":"1","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository contains the code, resources, and FAN1VS3 dataset related to the paper:\u003cbr\u003eH. Araghi, J. van Gemert, and N. Tomen, \"Pushing the Boundary of Event Subsampling in Event-Based Video Classification Using CNNs,\" in ECCV Workshop, 2024.\u003cbr\u003e# FAN1VS3 — an event-camera dataset of a fan at two rotation speeds\u003cbr\u003eFAN1VS3 is a small, two-class event-camera classification dataset. A desk fanwas recorded with a Prophesee Gen4.2 event camera at two of its rotation-speedsettings (`speed_1` and `speed_3`), and the two continuous recordings were cutinto short clips. The task is to decide, from a single clipof raw events, which speed setting the fan was running at.\u003cbr\u003e## Download\u003cbr\u003eThe dataset ships as a single ZIP archive, `FAN1VS3.zip`. After downloading, check it against `SHA256SUMS`:\u003cbr\u003e```bashsha256sum -c SHA256SUMS # Linux / macOS``````powershellGet-FileHash FAN1VS3.zip -Algorithm SHA256 # Windows PowerShell```\u003cbr\u003e## Layout\u003cbr\u003e```FAN1VS3/├── README.md├── LICENSE CC BY 4.0├── segmented/ ← the dataset proper│ ├── speed_1/speed_1_0000.hdf5 … speed_1_0234.hdf5 (235 clips)│ └── speed_3/speed_3_0000.hdf5 … speed_3_0274.hdf5 (275 clips)├── full_recordings/ ← optional, for your own segmentation│ ├── speed_1/{speed_1.hdf5, recording_2023-09-21_14-35-06.raw, ….bias}│ └── speed_3/{speed_3.hdf5, recording_2023-09-21_14-36-49.raw, ….bias}└── splits/ ├── training.txt validation.txt test.txt └── splits.csv relative_path, class_name, label_index, split```\u003cbr\u003e## Loading a clip\u003cbr\u003eEach clip in `segmented/` is an HDF5 file with a single `events` group holdingfour parallel, time-sorted arrays. Nothing beyond `h5py` and `numpy` is needed:\u003cbr\u003e```pythonimport h5py\u003cbr\u003ewith h5py.File(\"segmented/speed_1/speed_1_0000.hdf5\", \"r\") as f: x = f[\"events\"][\"xs\"][:] # int16 , pixel column y = f[\"events\"][\"ys\"][:] # int16 , pixel row t = f[\"events\"][\"ts\"][:] # float64 , microseconds, starts at 0 p = f[\"events\"][\"ps\"][:] # bool , True = ON (positive) event meta = dict(f.attrs) # num_events, num_pos, num_neg, duration, …\u003cbr\u003e# the label is carried by the file name: speed_1 -\u0026gt; 0, speed_3 -\u0026gt; 1label = {\"speed_1\": 0, \"speed_3\": 1}[\"speed_1_0000.hdf5\".rsplit(\"_\", 1)[0]]```\u003cbr\u003eThe root attributes are `num_events`, `num_pos` / `num_neg` (ON / OFF counts),`duration`, `t0` / `tk` (first and last timestamp, microseconds),`sensor_resolution` (`[1280, 720]`, width × height), and `num_imgs` / `num_flow`(both `0` — no frames or optical flow accompany this data).\u003cbr\u003eThe clips were written with the `hdf5_packager` from[`event_utils`](https://github.com/TimoStoff/event_utils), so any reader forthat format works unchanged.\u003cbr\u003eThe files in `full_recordings/` are Prophesee's own HDF5 export — events in`CD/events` as a structured array with fields `x`, `y`, `p`, `t` — alongside theoriginal `.raw` EVT3 stream and its `.bias` file; read those with the[Metavision SDK](https://docs.prophesee.ai/).\u003cbr\u003e## Recording and segmentation\u003cbr\u003eBoth recordings were made with a Prophesee Gen4.2 (1280×720)event camera pointed at a desk fan.\u003cbr\u003e| | `speed_1` | `speed_3` ||---|---|---|| Recording length | 17.34 s | 20.35 s || Total events | 111,000,469 | 81,484,980 || Clips | 235 | 275 || Events per full-length clip (min / mean / max) | 456,693 / 473,733 / 497,382 | 283,430 / 296,320 / 329,442 |\u003cbr\u003eEach recording was cut into contiguous, non-overlapping 74,000 µs windowsstarting at the timestamp of the recording's first event; each clip's timestamps are shifted so that `t0 = 0`.\u003cbr\u003e## Splits\u003cbr\u003eThe split is class-stratified: 75 % training, 10 % validation, 15 % test, drawnper class. `splits/*.txt` list one `class/filename.hdf5` per line;`splits/splits.csv` carries the same information with the class name and integerlabel (`speed_1 → 0`, `speed_3 → 1`).\u003cbr\u003e| Split | `speed_1` | `speed_3` | Total ||---|---|---|---|| training | 176 | 206 | 382 || validation | 23 | 27 | 50 || test | 36 | 42 | 78 || **all** | **235** | **275** | **510** |\u003cbr\u003e## License\u003cbr\u003eReleased under the **Creative Commons Attribution 4.0 International (CC BY 4.0)**license — see `LICENSE`. You may share and adapt the data for any purpose,including commercially, provided you give appropriate credit.\u003cbr\u003e## Citation\u003cbr\u003eIf you use this dataset in your research, please cite the following paper:\u003cbr\u003eH. Araghi, J. van Gemert, and N. Tomen, “Pushing the Boundaries of EventSubsampling in Event-Based Video Classification Using CNNs,” in *ComputerVision – ECCV 2024 Workshops*, Springer, 2025.[https://doi.org/10.1007/978-3-031-92460-6_17](https://doi.org/10.1007/978-3-031-92460-6_17)\u003cbr\u003e```bibtex@InProceedings{araghi2025pushing, author = {Araghi, Hesam and van Gemert, Jan and Tomen, Nergis}, title = {Pushing the Boundaries of Event Subsampling in Event-Based Video Classification Using {CNNs}}, booktitle = {Computer Vision -- ECCV 2024 Workshops}, pages = {276--292}, year = {2025}, publisher = {Springer Nature Switzerland}, doi = {10.1007/978-3-031-92460-6_17}}```\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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      <creatorName nameType="Personal">Hesam Araghi</creatorName>
      <givenName>Hesam</givenName>
      <familyName>Araghi</familyName>
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    <creator>
      <creatorName nameType="Personal">Nergis Tömen</creatorName>
      <givenName>Nergis</givenName>
      <familyName>Tömen</familyName>
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      <creatorName nameType="Personal">Jan van Gemert</creatorName>
      <givenName>Jan</givenName>
      <familyName>van Gemert</familyName>
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  <titles>
    <title>Code and data supporting the paper “Pushing the Boundary of Event Subsampling in Event-Based Video Classification Using CNNs”</title>
  </titles>
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  <publicationYear>2026</publicationYear>
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    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="09">Engineering</subject>
    <subject>Event cameras</subject>
    <subject>Event-based video classification</subject>
    <subject>event subsampling</subject>
    <subject>event-count accuracy trade-off</subject>
    <subject>Neuromorphic machine vision</subject>
  </subjects>
  <contributors>
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      <contributorName nameType="Organizational">TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Pattern Recognition &amp; Bioinformatics, Delft, The Netherlands</contributorName>
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    <date dateType="Issued">2026-08-11</date>
  </dates>
  <language>en</language>
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    <description descriptionType="Abstract">&lt;p&gt;This repository contains the code, resources, and FAN1VS3 dataset related to the paper:&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;H. Araghi, J. van Gemert, and N. Tomen, "Pushing the Boundary of Event Subsampling in Event-Based Video Classification Using CNNs," in ECCV Workshop, 2024.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;# FAN1VS3 — an event-camera dataset of a fan at two rotation speeds&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;FAN1VS3 is a small, two-class event-camera classification dataset. A desk fan&lt;/p&gt;&lt;p&gt;was recorded with a Prophesee Gen4.2 event camera at two of its rotation-speed&lt;/p&gt;&lt;p&gt;settings (`speed_1` and `speed_3`), and the two continuous recordings were cut&lt;/p&gt;&lt;p&gt;into short clips. The task is to decide, from a single clip&lt;/p&gt;&lt;p&gt;of raw events, which speed setting the fan was running at.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;## Download&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The dataset ships as a single ZIP archive, `FAN1VS3.zip`. After downloading, &lt;/p&gt;&lt;p&gt;check it against `SHA256SUMS`:&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;```bash&lt;/p&gt;&lt;p&gt;sha256sum -c SHA256SUMS            # Linux / macOS&lt;/p&gt;&lt;p&gt;```&lt;/p&gt;&lt;p&gt;```powershell&lt;/p&gt;&lt;p&gt;Get-FileHash FAN1VS3.zip -Algorithm SHA256   # Windows PowerShell&lt;/p&gt;&lt;p&gt;```&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;## Layout&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;```&lt;/p&gt;&lt;p&gt;FAN1VS3/&lt;/p&gt;&lt;p&gt;├── README.md&lt;/p&gt;&lt;p&gt;├── LICENSE           CC BY 4.0&lt;/p&gt;&lt;p&gt;├── segmented/         ← the dataset proper&lt;/p&gt;&lt;p&gt;│  ├── speed_1/speed_1_0000.hdf5 … speed_1_0234.hdf5  (235 clips)&lt;/p&gt;&lt;p&gt;│  └── speed_3/speed_3_0000.hdf5 … speed_3_0274.hdf5  (275 clips)&lt;/p&gt;&lt;p&gt;├── full_recordings/      ← optional, for your own segmentation&lt;/p&gt;&lt;p&gt;│  ├── speed_1/{speed_1.hdf5, recording_2023-09-21_14-35-06.raw, ….bias}&lt;/p&gt;&lt;p&gt;│  └── speed_3/{speed_3.hdf5, recording_2023-09-21_14-36-49.raw, ….bias}&lt;/p&gt;&lt;p&gt;└── splits/&lt;/p&gt;&lt;p&gt;  ├── training.txt validation.txt test.txt&lt;/p&gt;&lt;p&gt;  └── splits.csv       relative_path, class_name, label_index, split&lt;/p&gt;&lt;p&gt;```&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;## Loading a clip&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Each clip in `segmented/` is an HDF5 file with a single `events` group holding&lt;/p&gt;&lt;p&gt;four parallel, time-sorted arrays. Nothing beyond `h5py` and `numpy` is needed:&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;```python&lt;/p&gt;&lt;p&gt;import h5py&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;with h5py.File("segmented/speed_1/speed_1_0000.hdf5", "r") as f:&lt;/p&gt;&lt;p&gt;  x = f["events"]["xs"][:]   # int16  , pixel column&lt;/p&gt;&lt;p&gt;  y = f["events"]["ys"][:]   # int16  , pixel row&lt;/p&gt;&lt;p&gt;  t = f["events"]["ts"][:]   # float64 , microseconds, starts at 0&lt;/p&gt;&lt;p&gt;  p = f["events"]["ps"][:]   # bool  , True = ON (positive) event&lt;/p&gt;&lt;p&gt;  meta = dict(f.attrs)     # num_events, num_pos, num_neg, duration, …&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;# the label is carried by the file name: speed_1 -&amp;gt; 0, speed_3 -&amp;gt; 1&lt;/p&gt;&lt;p&gt;label = {"speed_1": 0, "speed_3": 1}["speed_1_0000.hdf5".rsplit("_", 1)[0]]&lt;/p&gt;&lt;p&gt;```&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The root attributes are `num_events`, `num_pos` / `num_neg` (ON / OFF counts),&lt;/p&gt;&lt;p&gt;`duration`, `t0` / `tk` (first and last timestamp, microseconds),&lt;/p&gt;&lt;p&gt;`sensor_resolution` (`[1280, 720]`, width × height), and `num_imgs` / `num_flow`&lt;/p&gt;&lt;p&gt;(both `0` — no frames or optical flow accompany this data).&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The clips were written with the `hdf5_packager` from&lt;/p&gt;&lt;p&gt;[`event_utils`](https://github.com/TimoStoff/event_utils), so any reader for&lt;/p&gt;&lt;p&gt;that format works unchanged.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The files in `full_recordings/` are Prophesee&amp;#x27;s own HDF5 export — events in&lt;/p&gt;&lt;p&gt;`CD/events` as a structured array with fields `x`, `y`, `p`, `t` — alongside the&lt;/p&gt;&lt;p&gt;original `.raw` EVT3 stream and its `.bias` file; read those with the&lt;/p&gt;&lt;p&gt;[Metavision SDK](https://docs.prophesee.ai/).&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;## Recording and segmentation&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Both recordings were made with a Prophesee Gen4.2 (1280×720)&lt;/p&gt;&lt;p&gt;event camera pointed at a desk fan.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;| | `speed_1` | `speed_3` |&lt;/p&gt;&lt;p&gt;|---|---|---|&lt;/p&gt;&lt;p&gt;| Recording length | 17.34 s | 20.35 s |&lt;/p&gt;&lt;p&gt;| Total events | 111,000,469 | 81,484,980 |&lt;/p&gt;&lt;p&gt;| Clips | 235 | 275 |&lt;/p&gt;&lt;p&gt;| Events per full-length clip (min / mean / max) | 456,693 / 473,733 / 497,382 | 283,430 / 296,320 / 329,442 |&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Each recording was cut into contiguous, non-overlapping 74,000 µs windows&lt;/p&gt;&lt;p&gt;starting at the timestamp of the recording&amp;#x27;s first event; each clip&amp;#x27;s timestamps &lt;/p&gt;&lt;p&gt;are shifted so that `t0 = 0`.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;## Splits&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The split is class-stratified: 75 % training, 10 % validation, 15 % test, drawn&lt;/p&gt;&lt;p&gt;per class. `splits/*.txt` list one `class/filename.hdf5` per line;&lt;/p&gt;&lt;p&gt;`splits/splits.csv` carries the same information with the class name and integer&lt;/p&gt;&lt;p&gt;label (`speed_1 → 0`, `speed_3 → 1`).&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;| Split | `speed_1` | `speed_3` | Total |&lt;/p&gt;&lt;p&gt;|---|---|---|---|&lt;/p&gt;&lt;p&gt;| training | 176 | 206 | 382 |&lt;/p&gt;&lt;p&gt;| validation | 23 | 27 | 50 |&lt;/p&gt;&lt;p&gt;| test | 36 | 42 | 78 |&lt;/p&gt;&lt;p&gt;| **all** | **235** | **275** | **510** |&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;## License&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)**&lt;/p&gt;&lt;p&gt;license — see `LICENSE`. You may share and adapt the data for any purpose,&lt;/p&gt;&lt;p&gt;including commercially, provided you give appropriate credit.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;## Citation&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;If you use this dataset in your research, please cite the following paper:&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;H. Araghi, J. van Gemert, and N. Tomen, “Pushing the Boundaries of Event&lt;/p&gt;&lt;p&gt;Subsampling in Event-Based Video Classification Using CNNs,” in *Computer&lt;/p&gt;&lt;p&gt;Vision – ECCV 2024 Workshops*, Springer, 2025.&lt;/p&gt;&lt;p&gt;[https://doi.org/10.1007/978-3-031-92460-6_17](https://doi.org/10.1007/978-3-031-92460-6_17)&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;```bibtex&lt;/p&gt;&lt;p&gt;@InProceedings{araghi2025pushing,&lt;/p&gt;&lt;p&gt; author  = {Araghi, Hesam and van Gemert, Jan and Tomen, Nergis},&lt;/p&gt;&lt;p&gt; title   = {Pushing the Boundaries of Event Subsampling in Event-Based&lt;/p&gt;&lt;p&gt;        Video Classification Using {CNNs}},&lt;/p&gt;&lt;p&gt; booktitle = {Computer Vision -- ECCV 2024 Workshops},&lt;/p&gt;&lt;p&gt; pages   = {276--292},&lt;/p&gt;&lt;p&gt; year   = {2025},&lt;/p&gt;&lt;p&gt; publisher = {Springer Nature Switzerland},&lt;/p&gt;&lt;p&gt; doi    = {10.1007/978-3-031-92460-6_17}&lt;/p&gt;&lt;p&gt;}&lt;/p&gt;&lt;p&gt;```&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;</description>
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