AirCode

This repo contains source codes for the arXiv preprint “AirCode: A Robust Object Encoding Method”

Demo

Object matching comparison when the objects are non-rigid and the view is changed, left is the result of our method while right is the result of NetVLAD

Relocalization on KITTI datasets

Dependencies

  • Python
  • PyTorch
  • OpenCV
  • Matplotlib
  • NumPy
  • Yaml

Data

Four datasets are used in our experiments.

KITTI Odometry

For relocalization experiment. Three sequences are selected, and they are “00”, “05” and “06”.

KITTI Tracking

For multi-object matching experiment. Four sequences are selected, and they are “0002”, “0003”, “0006”, “0010”.

VOT Datasets

For single-object matching experiment. We select three sequences from VOT2019 datasets and they are “bluecar”, “bus6” and “humans_corridor_occ_2_A”, because the tracked objects in these sequences are included in coco datasets, which are the data we used to train mask-rcnn.

OTB Datasets

For single-object matching experiment. We select five sequences and they are “BlurBody”, “BlurCar2”, “Human2”, “Human7” and “Liquor”.

Examples

Relocalization on KITTI Datasets

  1. Extract object descrptors

    python experiments/place_recogination/online_relocalization.py -c config/experiment_tracking.yaml -g 1 -s PATH_TO_SAVE_MIDDLE_RESULTS -d PATH_TO_DATASET -m PATH_TO_MODELS

  2. Compute precision-recall curves

    python experiments/place_recogination/offline_process.py -c config/experiment_tracking.yaml -g 1 -d PATH_TO_DATASET -n PATH_TO_MIDDLE_RESULTS -s PATH_TO_SAVE_RESULTS

  3. Compute top-K relocalization results

    python experiments/place_recogination/offline_topK.py -c config/experiment_tracking.yaml -g 1 -d PATH_TO_DATASET -n PATH_TO_MIDDLE_RESULTS -s PATH_TO_SAVE_RESULTS

Object Matching on OTB, VOT or KITTI Tracking Datasets

  • Run multi-object matching experiment in KITTI Tracking Datasets Modify the and run

    python experiments/object_tracking/object_tracking.py -c config/experiment_tracking.yaml -g 1 -s PATH_TO_SAVE_RESULTS -d PATH_TO_DATASET -m PATH_TO_MODELS

  • Run single-object matching experiment in OTB or VOT Datasets Modify the config file and run

    python experiments/object_tracking/single_object_tracking.py -c config/experiment_tracking.yaml -g 1 -s PATH_TO_SAVE_RESULTS -d PATH_TO_DATASET -m PATH_TO_MODELS

GitHub

GitHub - wang-chen/AirCode: AirCode: A Robust Object Encoding Method
AirCode: A Robust Object Encoding Method. Contribute to wang-chen/AirCode development by creating an account on GitHub.
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