EvTrack
Collection
models and datasets used in EvTrack • 3 items • Updated
Datasets:
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1405, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 982, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
txt string | __key__ string | __url__ string |
|---|---|---|
441,1613292313582444
442,1613292313582450
443,1613292313582451
444,1613292313582452
445,1613292313582452
446,1613292313582457
447,1613292313582458
448,1613292313582459
449,1613292313582461
450,1613292313582467
451,1613292313582469
452,1613292313582471
453,1613292313582482
454,1613292313582488
455,1613292313582489
456,1... | train_subset__dvSave-2021_02_14_16_45_13_car6__dvSave-2021_02_14_16_45_13_car6_timestamp2 | hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-000000.tar |
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
| train_subset__dvSave-2021_02_14_16_45_13_car6__absent_label | hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-000000.tar |
"1613292331214009, 78, 133, 1\n1613292331214009, 9, 144, 1\n1613292331214010, 5, 128, 1\n16132923312(...TRUNCATED) | train_subset__dvSave-2021_02_14_16_45_13_car6__dvSave-2021_02_14_16_45_13_car6_events | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
"139.6824,131.3195,23.7511,14.5146\n140.0123,131.9793,22.7615,13.525\n139.0227,131.9793,23.4213,13.8(...TRUNCATED) | train_subset__dvSave-2021_02_14_16_45_13_car6__groundtruth | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
"The 441 frame, the timestamp is 1613292313582444\nThe 442 frame, the timestamp is 1613292313582450\(...TRUNCATED) | train_subset__dvSave-2021_02_14_16_45_13_car6__dvSave-2021_02_14_16_45_13_timestamp_part | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
null | train_subset__dvSave-2021_02_14_16_45_13_car6__vis_imgs__frame0468 | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
null | train_subset__dvSave-2021_02_14_16_45_13_car6__vis_imgs__frame0449 | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
null | train_subset__dvSave-2021_02_14_16_45_13_car6__vis_imgs__frame0506 | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
null | train_subset__dvSave-2021_02_14_16_45_13_car6__vis_imgs__frame0486 | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
null | train_subset__dvSave-2021_02_14_16_45_13_car6__vis_imgs__frame0479 | "hf://datasets/krisspy39/visevent@2bfc9f20ecb391887bb2185f1a9618eb368f0021/webdataset/train/train-00(...TRUNCATED) |
End of preview.
VisEvent SOT Benchmark
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency. In practice, the visible cameras can better perceive texture details and slow motion, while event cameras can be free from motion blurs and have a larger dynamic range which enables them to work well under fast motion and low illumination. Therefore, the two sensors can cooperate with each other to achieve more reliable object tracking.
In this work, we propose a large-scale Visible-Event benchmark (termed VisEvent) due to the lack of a realistic and scaled dataset for this task. Our dataset consists of 820 video pairs captured under low illumination, high speed, and background clutter scenarios, and it is divided into a training and a testing subset, each of which contains 500 and 320 videos, respectively. Based on VisEvent, we transform the event flows into event images and construct more than 30 baseline methods by extending current single-modality trackers into dual-modality versions. More importantly, we further build a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data. Extensive experiments on the proposed VisEvent dataset, and two simulated datasets (i.e., OTB-DVS and VOT-DVS), validated the effectiveness of our model.
🏆 Attribution & Acknowledgements
Notice: This dataset is a mirror of the original VisEvent benchmark, uploaded to Hugging Face for easier access and integration via the datasets library.
All credit for the data collection, baseline methodologies, and cross-modality transformer design goes to the original researchers.
- Original Paper: VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
- Authors: Xiao Wang, Jianing Li, Lin Zhu, Zhipeng Zhang, Zhe Chen, Xin Li, Yaowei Wang, Yonghong Tian, Feng Wu
- Original Repository: https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark
📜 Citation
If you use this dataset in your research or project, please cite the original authors' work:
@article{wang2021viseventbenchmark,
title={VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows},
author={Xiao Wang, Jianing Li, Lin Zhu, Zhipeng Zhang, Zhe Chen, Xin Li, Yaowei Wang, Yonghong Tian, Feng Wu},
journal={arXiv:2108.05015},
year={2021}
}
- Downloads last month
- 763