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README.md

Video Analyst

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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 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}
}

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Maintainer (sorted by family name):

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

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