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https://github.com/ImmortalTracker/ImmortalTracker

Last synced: 4 months ago
JSON representation

Host: GitHub
URL: https://github.com/ImmortalTracker/ImmortalTracker
Owner: ImmortalTracker
Created: 2021-11-22T10:17:16.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2021-12-07T06:44:01.000Z (over 2 years ago)
Last Synced: 2024-01-16T05:33:20.771Z (5 months ago)
Language: Python
Size: 40 KB
Stars: 102
Watchers: 3
Forks: 18
Open Issues: 1
Metadata Files:
- Readme: README.md

Lists

Awesome-Autonomous-Driving - ImmortalTracker - Our mismatch ratio is tens of times lower than any previously published method. (3. Perception / 3.2 Tracking)
awesome-stars - ImmortalTracker/ImmortalTracker - (Python)

README

# Immortal_tracker
## Prerequisite
Our code is tested for Python 3.6.\
To install required liabraries:
```
pip install -r requirements.txt
```

## Waymo Open Dataset
### Prepare dataset & off-the-shelf detections
#### Download WOD perception dataset:
```
#Waymo Dataset
└── waymo
├── training (not required)
├── validation
├── testing
```
To extract timestamp infos/ego infos from .tfrecord files, run the following:
```
bash preparedata/waymo/waymo_preparedata.sh //waymo
```
Run the following to convert detection results into to .npz files. The detection results should be in official WOD submission format(.bin)
We recommand you to use CenterPoint(two-frame model for tracking) detection results for reproducing our results. Please follow https://github.com/tianweiy/CenterPoint or email its author for CenterPoint detection results.
```
bash preparedata/waymo/waymo_convert_detection.sh /detection_result.bin cp

#you can also use other detections:
#bash preparedata/waymo/waymo_convert_detection.sh
```

### Inference
Use the following command to start inferencing on WOD. The validation set is used by default.
```
python main_waymo.py --name immortal --det_name cp --config_path configs/waymo_configs/immortal.yaml --process 8
```

### Evaluation with WOD official devkit:
Follow https://github.com/waymo-research/waymo-open-dataset to build the evaluation tools and run the following command for evaluation:
```
#Convert the tracking results into .bin file
python evaluation/waymo/pred_bin.py --name immortal
#For evaluation
/bazel-bin/waymo_open_dataset/metrics/tools/compute_tracking_metrics_main mot_results/waymo/validation/immortal/bin/pred.bin /validation_gt.bin
```

## nuScenes Dataset
### Prepare dataset & off-the-shelf detections
#### Download nuScenes perception dataset
```
# For nuScenes Dataset
└── NUSCENES_DATASET_ROOT
├── samples
├── sweeps
├── maps
├── v1.0-trainval
├── v1.0-test
```
To extract timestamp infos/ego infos, run the following:

```
bash preparedata/nuscenes/nu_preparedata.sh /nuscenes
```

Run the following to convert detection results into to .npz files. The detection results should be in official nuScenes submission format(.json)
We recommand you to use centerpoint(two-frame model for tracking) detection results for reproducing our results.
```
bash preparedata/nuscenes/nu_convert_detection.sh /detection_result.json cp

#you can also use other detections:
#bash preparedata/nuscenes/nu_convert_detection.sh
```

### Inference
Use the following command to start inferencing on nuScenes. The validation set is used by default.
```
python main_nuscenes.py --name immortal --det_name cp --config_path configs/nu_configs/immortal.yaml --process 8
```

### Evaluation with nuScenes official devkit:
Follow https://github.com/nutonomy/nuscenes-devkit to build the official evaluation tools for nuScenes. Run the following command for evaluation:
```
#To convert tracking results into .json format
bash evaluation/nuscenes/pipeline.sh immortal
#To evaluate
python /nuscenes-devkit/python-sdk/nuscenes/eval/tracking/evaluate.py \
"./mot_results/nuscenes/validation_2hz/immortal/results/results.json" \
--output_dir "./mot_results/nuscenes/validation_2hz/immortal/results" \
--eval_set "val" \
--dataroot /nuscenes
```