Track Every Thing Accuracy

Track Every Thing in the Wild [ECCV 2022].

This is the official implementation of TETA metric describe in the paper.

The proposed TETA metric disentangles classification performance from tracking. Instead of using the predicted class labels to group per-class tracking results, we use location with the help of local cluster evaluation. We treat each ground truth bounding box of the target class as the anchor of each cluster and group prediction results inside each cluster to evaluate the localization and association performance. Our local clusters enable us to evaluate tracks even when the class prediction is wrong.

Install

Install the TETA environment using pip.

pip install -r requirements.txt

Go to the root of the teta folder and quick install by

pip install -e .

Support data format

Result format follows COCO-VID format. We describe the format in detail here

How to Run

Run on TAO.

python scripts/run_tao.py --METRICS TETA --TRACKERS_TO_EVAL TETer --GT_FOLDER ${GT_JSON_PATH}.json --TRACKER_SUB_FOLDER ${RESULT_JSON_PATH}.json   

Run on BDD100K.

python scripts/run_coco.py --METRICS TETA --TRACKERS_TO_EVAL TETer --GT_FOLDER ${GT_JSON_PATH}.json --TRACKER_SUB_FOLDER ${RESULT_JSON_PATH}.json   

Citation

@InProceedings{trackeverything,
  title = {Tracking Every Thing in the Wild},
  author = {Li, Siyuan and Danelljan, Martin and Ding, Henghui and Huang, Thomas E. and Yu, Fisher},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  month = {Oct},
  year = {2022}
}