megvii-research/video_analyst megvii-research/video_analyst

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megvii-research/video_analyst
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Repository Details

A series of basic algorithms that are useful for video understanding, including Single Object Tracking (SOT), Video Object Segmentation (VOS) and so on.

Video Analyst

Build Status

This is the implementation of a series of basic algorithms which is useful for video understanding, including Single Object Tracking (SOT), Video Object Segmentation (VOS), etc.

Current implementation list:

Example SiamFC++ outputs.

Example SAT outputs.

SOT Quick start

Setup

Please refer to SETUP.md, SOT_SETUP.md

Demo

SOT video demo

# demo with web camera
python3 ./demo/main/video/sot_video.py --config 'experiments/siamfcpp/test/vot/siamfcpp_alexnet.yaml' --device cuda --video "webcam" 

# demo with video file, and dump result into video file (optional)
python3 ./demo/main/video/sot_video.py --config 'experiments/siamfcpp/test/vot/siamfcpp_alexnet.yaml' --device cuda --video $video_dir/demo.mp4 --output $dump_path/result.mp4

# demo with extracted image files, and dump result into image files (optional)
python3 ./demo/main/video/sot_video.py --config 'experiments/siamfcpp/test/vot/siamfcpp_alexnet.yaml' --device cuda --video $video_dir/*.jpg --output $dump_dir

Test

Please refer to SOT_TEST.md for detail.

Training

Please refer to SOT_TRAINING.md for detail.

Repository structure (in progress)

project_root/
├── experiments  # experiment configurations, in yaml format
├── main
│   ├── train.py  # trainng entry point
│   └── test.py  # test entry point
├── video_analyst
│   ├── data  # modules related to data
│   │   ├── dataset  # data fetcher of each individual dataset
│   │   ├── sampler  # data sampler, including inner-dataset and intra-dataset sampling procedure
│   │   ├── dataloader.py  # data loading procedure
│   │   └── transformer  # data augmentation
│   ├── engine  # procedure controller, including traiing control / hp&model loading
│   │   ├── monitor  # monitor for tasks during training, including visualization / logging / benchmarking
│   │   ├── trainer.py  # train a epoch
│   │   ├── tester.py  # test a model on a benchmark
│   ├── model # model builder
│   │   ├── backbone  # backbone network builder
│   │   ├── common_opr  # shared operator (e.g. cross-correlation)
│   │   ├── task_model  # holistic model builder
│   │   ├── task_head  # head network builder
│   │   └── loss  # loss builder
│   ├── pipeline  # pipeline builder (tracking / vos)
│   │   ├── segmenter  # segmenter builder for vos
│   │   ├── tracker  # tracker builder for tracking
│   │   └── utils  # pipeline utils
│   ├── config  # configuration manager
│   ├── evaluation  # benchmark
│   ├── optim  # optimization-related module (learning rate, gradient clipping, etc.)
│   │   ├── optimizer # optimizer
│   │   ├── scheduler # learning rate scheduler
│   │   └── grad_modifier # gradient-related operation (parameter freezing)
│   └── utils  # useful tools
└── README.md

docs

For detail, please refer to markdown files under docs.

SOT

VOS

DEVELOP

TODO

[] refine code stype and test cases

Acknowledgement

References

@inproceedings{xu2020siamfc++,
  title={SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines.},
  author={Xu, Yinda and Wang, Zeyu and Li, Zuoxin and Yuan, Ye and Yu, Gang},
  booktitle={AAAI},
  pages={12549--12556},
  year={2020}
}

@inproceedings{chen2020state,
  title={State-Aware Tracker for Real-Time Video Object Segmentation},
  author={Chen, Xi and Li, Zuoxin and Yuan, Ye and Yu, Gang and Shen, Jianxin and Qi, Donglian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9384--9393},
  year={2020}
}

Contact

Maintainer (sorted by family name):

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