https://github.com/sensorsini/mvsec-nightl21
A labeled dataset from a subset of the MVSEC dataset for car detection at night driving conditions.
https://github.com/sensorsini/mvsec-nightl21
event-camera neuromorphic-engineering object-detection
Last synced: 8 days ago
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A labeled dataset from a subset of the MVSEC dataset for car detection at night driving conditions.
- Host: GitHub
- URL: https://github.com/sensorsini/mvsec-nightl21
- Owner: SensorsINI
- Created: 2021-06-14T14:51:16.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-06-21T15:49:45.000Z (almost 4 years ago)
- Last Synced: 2025-04-04T03:04:41.683Z (about 2 months ago)
- Topics: event-camera, neuromorphic-engineering, object-detection
- Language: Python
- Homepage:
- Size: 39.1 KB
- Stars: 13
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MVSEC-NIGHTL21
https://user-images.githubusercontent.com/939553/122247023-57b18e80-cec7-11eb-8ac9-ed6bb88c1095.mp4
## Citation
[](https://doi.org/10.5281/zenodo.4967574)
When use this dataset, please cite:
```bibtex
@InProceedings{Hu_2021_CVPR,
author = {Hu, Yuhuang and Liu, Shih-Chii and Delbruck, Tobi},
title = {v2e: From Video Frames to Realistic DVS Events},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021},
pages = {1312-1321}
}
```
## Parent dataset: MVSEC
MVSEC-NIGHTL21 is derived dataset of "The Multi Vehicle Stereo Event Camera Dataset" which is available here: https://daniilidis-group.github.io/mvsec/
Please also cite the original MVSEC paper:
+ Zhu, A. Z., Thakur, D., Ozaslan, T., Pfrommer, B., Kumar, V., & Daniilidis, K. (2018). The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception. IEEE Robotics and Automation Letters, 3(3), 2032-2039.
## Usage
[](https://colab.research.google.com/drive/1qL8LoCZ-mm_O8K3aMDo22M3KSUtaK0cp?usp=sharing)
### Data Description
### MVSEC at night condition
We used `outdoor_night1_data.hdf5` of the MVSEC dataset.
The dataset is recorded with dual camera, we use the `left` camera.
In the HDF5 archive, the relevant dataset can be accessed as following:
```python
mvsec_data = h5py.File(mvsec_data_path, "r")
# raw frame
frame_data = mvsec_data["davis"]["left"]["image_raw"]
# frame timestamps
frame_ts = mvsec_data["davis"]["left"]["image_raw_ts"]
# raw events
events_data = mvsec_data["davis"]["left"]["events"]
# event indices that corresponds to the frame
frame_event_inds = mvsec_data["davis"]["left"]["image_raw_event_inds"]
```
For visualization in this repository, we only used the raw frames.
### MVSEC-NIGHTL21 Labels
In the validation set, there are 400 frames. The list of the frame indices is in [`frame_list.txt`](./frame_list.txt).
Among these 400 frames, 368 frames are labeled. The frames that don't have labels are listed in [`frames_that_dont_have_labels.txt`](./frames_that_dont_have_labels.txt).
We labelled `car` in these frames.
The labelled groundtruths are stored in `.txt` files and can be found in [`mvsec_nightl21_labels`](./mvsec_nightl21_labels).
Each labeled car is in the format `car x_min y_min x_max y_max`. For example:
```
car 48 112 143 170
```
means `x_min=48, y_min=112`, and `x_max=143, y_max=170`.
### Visualization
1. Install dependency
```
pip install h5py
pip install matplotlib
pip install opencv-python
```
2. Clone this repository
```
git clone https://github.com/SensorsINI/MVSEC-NIGHTL21
cd MVSEC-NIGHTL21
```
3. Download the `outdoor_night1_data.hdf5` from MVSEC dataset, available [here](https://drive.google.com/drive/folders/1rwyRk26wtWeRgrAx_fgPc-ubUzTFThkV)
4. Run The Visualization
```
python visualize_mvsec_nightl21.py --mvsec_data /path/to/outdoor_night1_data.hdf5 --gt_root ./mvsec_nightl21_labels
```
If everything works, you should see a video that annotates the cars.
## Contact
Yuhuang Hu
[email protected]