memoryunreal/corruption-invariant-tracking-benchmark

Corruption-Invariant-Tracking-Benchmark (CITB)

cvpr CITB is a rigorous benchmark established for tracking corruption robustness by re-constructing both single- and multi-modal tracking datasets with task-specific corruptions, resulting in VOT2020ST-C, GOT-10k-C, VOT2020-LT-C, UAV20L-C, and DepthTrack-C datasets.

Execute environment

server: 172.18.36.46 
docker image: watchtowerss/cvpr2023:latest
container ID: 9e61af2c327d
project path: /home/CVPR2023/Corruption-Invariant-Tracking-Benchmark
dataset: /home/dataset4/

dataset-generate:
env: pytracking

Installation

For all the tracking methods are using 'for' loop instead of dataloader to load dataset in inference stage, to accelerate the tracking speed, we create the corruption dataset in advance.

• Environment install

cd Corruption-Invariant-Tracking-Benchmark/dist/
python setup.py install

• Generate corruption datset

# generate image corruption dataset
python random_corruption_datset.py 

# generate video corruption dataset
python video_corruption.py 

# random corruption fo UAV20L
python random_20l.py

• vot2020 and votlt2020

bash launchfile/vot.sh

• GOT10K

Corruption Settings

Benchmark Datasets Construction

Dataset	Description	#Videos	Added Corruptions
VOT2020-ST	Short-term tracking	60	Noise; Blur; Digital
GOT-10k	Short-term tracking	180	Noise; Blur; Digital
VOT2020-LT	Long-term tracking	50	Noise; Blur; Digital; Transmission
UAV20L	UAV tracking	20	Noise; Blur; Digital; Weather; Transmission
DepthTrack	RGB-Depth Tracking	50	Noise; Blur; Digital; Transmission; Multi-modal

pytracking trackers

# run the pytracking trackers dimp tomp keep_track eco kys lwl atom(failed)
/launchfile/pytracking/*.sh

$