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MMCT

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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:    RuntimeError
Message:      File '1/A/jpg/000001.jpg' is encrypted, password required for extraction
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 328, in compute
                  compute_first_rows_from_parquet_response(
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
                  rows_index = indexer.get_rows_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 631, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 512, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 529, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 91, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 183, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 68, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1392, in __iter__
                  example = _apply_feature_types_on_example(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1082, in _apply_feature_types_on_example
                  decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1940, in decode_example
                  return {
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1941, in <dictcomp>
                  column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1341, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 180, in decode_example
                  with xopen(path, "rb", download_config=download_config) as f:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 512, in xopen
                  file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 132, in open
                  return self.__enter__()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 100, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1307, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 120, in _open
                  out = self.zip.open(path, mode.strip("b"))
                File "/usr/local/lib/python3.9/zipfile.py", line 1563, in open
                  raise RuntimeError("File %r is encrypted, password "
              RuntimeError: File '1/A/jpg/000001.jpg' is encrypted, password required for extraction

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Towards Effective Multi-Moving-Camera Tracking: A New Dataset and Lightweight Link Model

This repository contains the details of the dataset and the Pytorch implementation of the Paper: Towards Effective Multi-Moving-Camera Tracking: A New Dataset and Lightweight Link Model

Abstract

Ensuring driving safety for autonomous vehicles has become increasingly crucial, highlighting the need for systematic tracking of on-road pedestrians. Most vehicles are equipped with visual sensors, however, the large-scale visual data has not been well studied yet. Multi-target multi-camera (MTMC) tracking systems are composed of two modules: single-camera tracking (SCT) and inter-camera tracking (ICT). To reliably coordinate between them, MTMC tracking has been a very complicated task, while tracking across multiple moving cameras makes it even more challenging. In this paper, we focus on multi-target multi-moving-camera (MTMMC) tracking, which is attracting increasing attention from the research community. Observing there are few datasets for MTMMC tracking, we collect a new dataset, called Multi-Moving-Camera Track (MMCT), which contains sequences under various driving scenarios. To address the common problems of identity switch easily faced by most existing SCT trackers, especially for moving cameras due to ego-motion between the camera and targets, a lightweight appearance-free global link model, called Linker, is proposed to mitigate the identity switch by associating two disjoint tracklets of the same target into a complete trajectory within the same camera. Incorporated with Linker, existing SCT trackers generally obtain a significant improvement. Moreover, to alleviate the impact of the image style variations caused by different cameras, a color transfer module is effectively incorporated to extract cross-camera consistent appearance features for pedestrian association across moving cameras for ICT, resulting in a much improved MTMMC tracking system, which can constitute a step further towards coordinated mining of multiple moving cameras.

Dataset Description

We collect data in 12 distinct scenarios: ''A', 'B', 'C',...'L''. Each scenario may include the interaction of two or three cameras on different cars. For example, scene A includes two sequences of A-I and A-II. There are 32 sequences in total.

Dataset Structure 

MMCT
├── data
│ ├── gps
│ └── labelS
└── images
 ├── 1
 │ ├── A
 │ │ ├── IMG_0098-frag-s1-a-fps5.mp4
 │ │ └── jpg
 │ └── C
 │ ├── IMG_0559-frag-s1-c-fps5.mp4
 │ ├── jpg
 ├── 2
 │ ├── A
 │ │ ├── IMG_0094-frag-s2-a-fps5.mp4
 │ │ ├── jpg
 │ ├── B
 │ │ ├── IMG_2248-frag-s2-b-fps5.mp4
 │ │ ├── jpg
 ...
 ├── 12
 │ ├── A
 │ │ ├── IMG_0104-frag-s12-a-fps5.mp4
 │ │ ├── jpg
 │ ├── B
 │ │ ├── IMG_2254-frag-s12-b-fps5.mp4
 │ │ ├── jpg
 │ └── C
 │ ├── IMG_0569-frag-s12-c-fps5.mp4
 │ ├── jpg

Dataset Downloads

The whole dataset can be downloaded from Huggingface. Note that each file needs to unzip by the password. You can decompress each .zip file in its folder after sending us (2212534@mail.dhu.edu.cn, ytzhang@dhu.edu.cn) the LICENSE. in any format.

Reference

The license agreement for data usage implies the citation of the paper above. Please notice that citing the dataset URL instead of the publications would not be compliant with this license agreement. You can read the LICENSE from LICENSE.

Contact

If you have any concerns, please contact 2212534@mail.dhu.edu.cn

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