Fast-Deep-OC-SORT

Installation

As in Deep-OC-SORT, follow the following instructions.

After cloning, install external dependencies:

cd external/YOLOX/
pip install -r requirements.txt && python setup.py develop
cd ../deep-person-reid/
pip install -r requirements.txt && python setup.py develop
cd ../fast_reid/
pip install -r docs/requirements.txt

OCSORT dependencies are included in the external dependencies. If you're unable to install faiss-gpu needed by fast_reid, faiss-cpu should be adequate. Check the external READMEs for any installation issues.

Add the weights to the external/weights directory (do NOT untar the .pth.tar YOLOX files).

Data

Place MOT17/20 and DanceTrack under:

data
|——————mot (this is MOT17)
|        └——————train
|        └——————test
|——————MOT20
|        └——————train
|        └——————test
|——————dancetrack
|        └——————train
|        └——————test
|        └——————val

and run:

python3 data/tools/convert_mot17_to_coco.py
python3 data/tools/convert_mot20_to_coco.py
python3 data/tools/convert_dance_to_coco.py

Tracking

For Deep-OC-SORT, which is the baseline, run:

exp=best_paper_ablations
python3 main.py --exp_name $exp --post --grid_off --new_kf_off --dataset mot17 --w_assoc_emb 0.75 --aw_param 0.5
python3 main.py --exp_name $exp --post --grid_off --new_kf_off --dataset mot20 --track_thresh 0.4 --w_assoc_emb 0.75 --aw_param 0.5
python3 main.py --exp_name $exp --post --grid_off --new_kf_off --dataset dance --aspect_ratio_thresh 1000 --w_assoc_emb 1.25 --aw_param 1

For Fast-Deep-OC-SORT add the following flags:

exp=best_paper_ablations
python3 main.py --exp_name $exp --post --grid_off --new_kf_off --dataset mot17 --w_assoc_emb 0.75 --aw_param 0.5 --occlusion_threshold {IoU_threshold} --aspect_ratio_threshold {aspect_ratio_threshold}
python3 main.py --exp_name $exp --post --grid_off --new_kf_off --dataset mot20 --track_thresh 0.4 --w_assoc_emb 0.75 --aw_param 0.5 --occlusion_threshold {IoU_threshold} --aspect_ratio_threshold {aspect_ratio_threshold}
python3 main.py --exp_name $exp --post --grid_off --new_kf_off --dataset dance --aspect_ratio_thresh 1000 --w_assoc_emb 1.25 --aw_param 1 --occlusion_threshold {IoU_threshold} --aspect_ratio_threshold {aspect_ratio_threshold}

where {IoU_threshold} and {aspect_ratio_threshold} are the parameters that are introduced in Fast-Deep-OC-SORT, and explained in the paper.

Evaluation

To run TrackEval for HOTA and Identity with linear post-processing on MOT17, run:

python3 external/TrackEval/scripts/run_mot_challenge.py \
  --SPLIT_TO_EVAL val \
  --METRICS HOTA Identity \
  --TRACKERS_TO_EVAL ${exp}_post \
  --GT_FOLDER results/gt/ \
  --TRACKERS_FOLDER results/trackers/ \
  --BENCHMARK MOT17