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• [ICCV'2023] - Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking

  Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking<br> accepted by ICCV2023<br>

  This is an official pytorch implementation of the ICCV2023 paper Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking, a new framework for Efficient visual object tracking.

  Highlights

  Simple architecture

  The architecture of HiT is very simple only contains three components: a lightweight hierarchical vision transformer, a bridge Module and a prediction head.

  

  Strong performance

  HiT achieves promising speed with competitive performance, it is faster and performs better than previous efficient trackers, It significantly closes the gap between efficient trackers and mainstream trackers. It can be deployed in low-computing power scenarios.

  

  	HiT-Base	HiT-Small	HiT-Tiny	FEAR	TransT
  LaSOT (AUC)	64.6	60.4	54.7	53.5	64.9
  GOT-10K (AO)	64.0	62.6	52.6	61.9	72.3
  TrackingNet (AUC)	80.0	77.7	74.6	-	81.4
  PyTorch Speed on 3090 GPU (FPS)	175	192	204	105	63
  PyTorch Speed on i9-9900K CPU (FPS)	33	72	76	60	5
  PyTorch Speed on Nvidia Jetson AGX (FPS)	61	68	77	38	13
  PyTorch Speed on Nvidia Jetson NX (FPS)	32	34	39	22	11
  ONNX Speed on 3090 GPU (FPS)	274	400	455	-	-
  ONNX Speed on i9-9900K CPU (FPS)	42	98	125	-	-
  ONNX Speed on Nvidia Jetson AGX (FPS)	75	119	145	-	-

  Install the environment

  Option1: Use the Anaconda

  conda create -n HIT python=3.8
  conda activate HIT
  pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
  pip install -r requirements.txt
  

Data Preparation

Put the tracking datasets in ./data. It should look like:

${STARK_ROOT}
-- data
  -- lasot
      |-- airplane
      |-- basketball
      |-- bear
      ...
  -- got10k
      |-- test
      |-- train
      |-- val
  -- coco
      |-- annotations
      |-- images
  -- trackingnet
      |-- TRAIN_0
      |-- TRAIN_1
      ...
      |-- TRAIN_11
      |-- TEST

Set project paths

Run the following command to set paths for this project

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir .

After running this command, you can also modify paths by editing these two files

lib/train/admin/local.py  # paths about training
lib/test/evaluation/local.py  # paths about testing

Train HiT

python -m torch.distributed.launch --nproc_per_node 8 lib/train/run_training.py --script HiT --config HiT_Base --save_dir .

(Optionally) Debugging training with a single GPU

python lib/train/run_training.py --script HiT --config HiT_Base --save_dir .

Test and evaluate on benchmark

• LaSOT

python tracking/test.py HiT HiT_Base --dataset lasot --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names

• GOT10K-test

python tracking/test.py HiT HiT_Base --dataset got10k_test --threads 2 --num_gpus 2 --debug 0
python lib/test/utils/transform_got10k.py --tracker_name HiT --cfg_name HiT_Base

• TrackingNet

python tracking/test.py HiT HiT_Base --dataset trackingnet --threads 2 --num_gpus 2 --debug 0
python lib/test/utils/transform_trackingnet.py --tracker_name HiT --cfg_name HiT_Base

• NFS

python tracking/test.py HiT HiT_Base --dataset nfs --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names

• UAV123

python tracking/test.py HiT HiT_Base --dataset uav --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names

• LaSOText

python tracking/test.py HiT HiT_Base --dataset lasot_extension_subset --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names

Run Video demo

python tracking/video_demo.py <path of onnx model> <video path> 

Transform onnx; test speed, flops, params

python tracking/transfer_onnx
python tracking/profile_model_hit --script HiT --config HiT_Base
python tracking/profile_model_hit_cpu --script HiT --config HiT_Base 
python tracking/profile_model_hit_onnx --script HiT --config HiT_Base
python tracking/profile_model_hit_onnx_cpu --script HiT --config HiT_Base

Models && Raw results

The trained models, and the raw tracking results are provided in here

Acknowledgement

• This codebase is implemented on STARK and PyTracking.We would like to thank their authors for providing great libraries.

Contact

• Ben Kang (email:kangben@mail.dlut.edu.cn)

• Xin Chen (email:chenxin3131@mail.dlut.edu.cn)

  Feel free to contact if you have additional questions.