peterzhousz / video_analyst

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

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:

• SOT
  • SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines [demo]

Example SiamFC++ outputs.

Quick start

Setup

Please refer to SETUP.md

Test

Test on VOT

python3 ./main/test.py --config 'experiments/siamfcpp/test/vot/siamfcpp_alexnet.yaml'

Test on GOT-10k

python3 ./main/test.py --config 'experiments/siamfcpp/test/got10k/siamfcpp_alexnet-got.yaml'

Please refer to docs/TEST.md for detail.

Training

From epoch 0

python3 ./main/train.py --config 'experiments/siamfcpp/train/siamfcpp_alexnet-trn.yaml'

Resuming (e.g. from epoch 10)

python3 ./main/train.py --config 'experiments/siamfcpp/train/siamfcpp_alexnet-trn.yaml' --resume-from-epoch=10

Please refer to docs/TRAINING.md for detail.

Repository structure (in progress)

├── 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
│   │   ├── hook  # hook 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.

• SETUP.md: instructions for setting-up
• MODEL_ZOO.md: description of released models
• TRAINING.md: details related to training
• DEVELOP.md: description of project design (registry, configuration tree, etc.)
• PIPELINE_API.md: description for pipeline API
• FORMATTING_INSTRUCTION: instruction for code formatting (yapf/isort/flake/etc.)

TODO

• [] Training code
  • [] LaSOT
  • [] COCO
• [] Test code for OTB, LaSOT, TrackingNet

Acknowledgement

• video_analyst/evaluation/vot_benchmark and other related code have been borrowed from PySOT
• video_analyst/evaluation/got_benchmark and other related code have been borrowed from got-toolkit

Contact

Maintainer (sorted by family name):

• Xi Chen@XavierCHEN34
• Zuoxin Li@lzx1413
• Zeyu Wang@JWarlock
• Yinda Xu@MARMOTatZJU