This repo contains source codes for the arXiv preprint “AirCode: A Robust Object Encoding Method”
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
Four datasets are used in our experiments.
For relocalization experiment. Three sequences are selected, and they are “00”, “05” and “06”.
For multi-object matching experiment. Four sequences are selected, and they are “0002”, “0003”, “0006”, “0010”.
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.
For single-object matching experiment. We select five sequences and they are “BlurBody”, “BlurCar2”, “Human2”, “Human7” and “Liquor”.
Relocalization on KITTI Datasets
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
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
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