FastTracker: Real-Time and Accurate Visual Tracking
Paper
• 2508.14370 • Published
Datasets:
Tasks:
Object Detection
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
Multi-object-tracking
License:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: UnidentifiedImageError
Message: cannot identify image file <_io.BytesIO object at 0x7fd8c247b650>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2061, in __iter__
batch = formatter.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2161, in decode_batch
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1419, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 190, in decode_example
image = PIL.Image.open(bytes_)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3498, in open
raise UnidentifiedImageError(msg)
PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7fd8c247b650>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
FastTracker Benchmark
A new benchmark dataset comprising diverse vehicle classes with frame-level tracking annotation introduced in paper: FastTracker: Real-Time and Accurate Visual Tracking
Hamidreza Hashempoor, Yu Dong Hwang.
Updates
| Date | Update |
|---|---|
| 2026-03-01 | Speciall thanks to _Mikhail Kozak which helped to prepare the revised current version of the benchmark. |
Resources
| Github | Paper |
|---|---|
 |
 |
Dataset Overview
Brief statistics and visualization of FastTracker benchmark and its comparison with other benchmarks.
| Attribute | UrbanTracker | CityFlow | FastTracker |
|---|---|---|---|
| Year | 2014 | 2022 | 2025 |
| Detections | 12.5K | 890K | 800K |
| #Videos | 5 | 40 | 12 |
| Obj/Frame | 5.4 | 8.2 | 43.5 |
| #Classes | 3 | 1 | 9 |
| #Scenarios | 1 | 4 | 12 |
Dataset Summary
What is it?
FastTrack is a large-scale benchmark dataset for evaluating multi-object tracking in complex and high-density traffic environments. It includes 800K annotated object detections across 12 videos, with an average of 43.5 objects per frame. The dataset features 9 traffic-related classes and covers diverse real-world traffic scenarios—such as multilane intersections, tunnels, crosswalks, and merging roads—captured under varying lighting conditions (daytime, nighttime, shadows).Why was it created?
FastTrack was created to address limitations of existing benchmarks like UrbanTracker and CityFlow, which lack diversity in scene types and have lower object density. This benchmark introduces challenging conditions including extreme crowding, long-term occlusions, and diverse motion patterns, to push the boundaries of modern multi-object tracking algorithms—particularly those optimized for real-world, urban traffic settings.What can it be used for?
Multi-object tracking, re-identification, online tracking evaluation, urban scene understanding, and benchmarking tracking algorithms under occlusion and crowding.Who are the intended users?
Researchers and practitioners in computer vision and intelligent transportation systems, especially those focusing on real-time tracking, urban mobility, autonomous driving, and edge deployment. Also valuable for students and developers working on lightweight or environment-aware tracking models.
Dataset Structure
Data Format
Each sequence is provided as a compressed archive inside the train/ directory:
train/
task_day_left_turn.zip
task_day_occlusion.zip
...
After extracting a sequence archive, the structure is:
task_xxx/
img1/ # extracted frames (.jpg)
gt/ # MOT-format ground truth (gt.txt)
video/ # reconstructed video (.mp4) and metadata
seqinfo.ini # sequence metadata
GT formats gt/gt.txt is like (each line):
frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, visibility.
Each sequence includes seqinfo.ini:
[Sequence]
name=task_day_left_turn
imDir=img1
frameRate=30
seqLength=1962
imWidth=1920
imHeight=1080
imExt=.jpg
Citation
If you use our code or Benchmark, please cite our work.
@misc{hashempoor2025fasttrackerrealtimeaccuratevisual,
title={FastTracker: Real-Time and Accurate Visual Tracking},
author={Hamidreza Hashempoor and Yu Dong Hwang},
year={2025},
eprint={2508.14370},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.14370},
}
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