non-overlapping_multiple-camera_tracking
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Published
20 hours ago •
lockxuan
lockxuan
Multi-Target Multi-Camera Human Tracking (Non-overlapping camera system)
non-overlapping_multiple-camera_tracking
This work references An equalized global graph model-based approach for multi-camera object tracking.
Dataset
This work uses NLPR_MCT dataset
After downloading the dataset, run dat2csv.py to change dat files to csv files.
python dat2csv.py --source PATH/TO/ANNOTATION --dataset DATASET_NO
Method
The association algorithm is iterative min-cost-flow.
The overview approach is shown in below:
Experiments
The evaluation metrics including MCTA, mmes, and mmec.
Dataset 1
| Method | MCTA ↑ | mmec (inter) ↓ | mmes (intra) ↓ |
|---|---|---|---|
| EGM | 85.25 % | 49 | 66 |
| IMCF (this work) | 84.01 % | 53 | 59 |
Dataset 2
| Method | MCTA ↑ | mmec (inter) ↓ | mmes (intra) ↓ |
|---|---|---|---|
| EGM | 73.7 % | 93 | 107 |
| IMCF (this work) | 85.76 % | 60 | 110 |
Dataset 3
| Method | MCTA ↑ | mmec (inter) ↓ | mmes (intra) ↓ |
|---|---|---|---|
| EGM | 47.24 % | 80 | 51 |
| IMCF (this work) | 31.77 % | 133 | 82 |
Dataset 4
| Method | MCTA ↑ | mmec (inter) ↓ | mmes (intra) ↓ |
|---|---|---|---|
| EGM | 37.78 % | 159 | 128 |
| IMCF (this work) | 28.52 % | 177 | 166 |
How to run?
requirements
- opencv-python==4.5.5
- torchreid
showing videos
python display.py --dataset NO. --data_path PATH/TO/NPLR/DATA --annotation PATH/TO/ANNOTATION --bbox
tracking
run the modules seperately (take sub-dataset 1 as example)
python run_sct.py --dataset 1 --cid 1
python run_sct.py --dataset 1 --cid 2
python run_sct.py --dataset 1 --cid 3
python run_mct.py --dataset 1
NOTE the reid model is downloaded from torchreid model zoo
lockxuan