Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/chkim403/blstm-mtp
Official Tensorflow Implementation of "Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking"
https://github.com/chkim403/blstm-mtp
Last synced: about 1 month ago
JSON representation
Official Tensorflow Implementation of "Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking"
- Host: GitHub
- URL: https://github.com/chkim403/blstm-mtp
- Owner: chkim403
- License: mit
- Created: 2021-01-20T05:39:25.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-01-04T04:56:35.000Z (almost 3 years ago)
- Last Synced: 2024-08-03T22:14:03.490Z (5 months ago)
- Language: Python
- Homepage:
- Size: 48.8 KB
- Stars: 22
- Watchers: 3
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-multiple-object-tracking - [code
README
# BLSTM-MTP
This repository contains the official Tensorflow implementation of [Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking (CVPR 2021)](https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Discriminative_Appearance_Modeling_With_Multi-Track_Pooling_for_Real-Time_Multi-Object_Tracking_CVPR_2021_paper.html).
## Dependencies
The code has been tested with:
- Python 3.7.6
- Tensorflow 1.15
- CUDA 10.0
- cuDNN 7.6.5
- OpenCV 4.5.3
## Download data
1. Download the MOT17 Challenge dataset from [this link](https://drive.google.com/file/d/1lZGLxWUcpRoVl0QGuPUx_ry10FqklaIf/view?usp=sharing). The zip file includes MOT Challenge public detections processed by [Tracktor](https://github.com/phil-bergmann/tracking_wo_bnw). We use this version of the public detections in our tracking demo below.
2. If you already downloaded the dataset from the official MOT Challenge [website](https://motchallenge.net/) before, please download the data from [this link](https://drive.google.com/file/d/1LSda5Z44qJZX9K50PrvXHf8exrPKHvRy/view?usp=sharing) instead which doesn't include the image files.
## Demo
1. Set `DATASET_DIR` in `config_tracker.py` to your own directory where the dataset you download above is located.
2. If you want to write the tracking output as images as well, set `IS_VIS` in `config_tracker.py` to `True`. Otherwise, leave it as it is.
3. Download the model file from [here](https://drive.google.com/file/d/1dOYof9N8RhFACS5dkr-gLQcb8eTOXupJ/view?usp=sharing) and unzip the file. Use the location where the checkpoint file is located as `model_path` in the command below.
4. Run the following command. Use your own paths for `model_path` and `output_path`. As for `detector`, you can use one of `DPM`, `FRCNN`, and `SDP`.
```
python run_tracker.py --model_path=YOUR_MODEL_FOLDER/model.ckpt --output_path=YOUR_OUTPUT_FOLDER --detector=FRCNN --threshold=0.5 --network_type=appearance_motion_network
```
5. This command will generate the tracking result that is shown in Table 6 of our paper. You can use these [files](https://drive.google.com/file/d/1CKdjUIlHbfO304IYwsrdblQ_PXEtmYCm/view?usp=sharing) to verify your output files.
## Performance
When paired with [Tracktor](https://github.com/phil-bergmann/tracking_wo_bnw) or [CenterTrack](https://github.com/xingyizhou/CenterTrack), our method greatly improves the tracking performance in terms of IDF1 and IDS.
| Method | IDF1 | MOTA | IDS | MT | ML | Frag | FP | FN |
| --------------------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
| Tracktor++v2 | 55.1 | 56.3 | 1,987 | 21.1 | 35.3 | 3,763 | 8,866 | 235,449 |
| Ours + Tracktor++v2 | 60.5 | 55.9 | 1,188 | 20.5 | 36.7 | 4,185 | 8,663 | 238,863 |
The data file that you download in the instructions above also includes MOT Challenge detections processed by CenterTrack (`centertrack_prepr_det.txt`). In order to use it as input to the tracker, you can simply change `run_tracker.py` in a way that it reads detections from `centertrack_prepr_det.txt` instead of `tracktor_prepr_det.txt`. The following is the result obtained by using the public detections processed by CenterTrack.
| Method | IDF1 | MOTA | IDS | MT | ML | Frag | FP | FN |
| --------------------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
| CTTrackPub | 59.6 | 61.5 | 2,583 | 26.4 | 31.9 | 4,965 | 14,076 | 200,672 |
| Ours + CTTrackPub | 62.9 | 62.0 | 1,750 | 27.9 | 31.0 | 7,433 | 17,621 | 194,946 |
With NVIDIA TITAN Xp, the inference code runs at around 24 fps on the MOT17 Challenge test set (excluding time spent on I/O operations).
## Training
The training code will be released soon in the future release. Stay tuned for more updates.
## License
The code is released under the MIT License.
## Contact
If you have any questions, please contact me at [email protected].
## Citation
```
@InProceedings{Kim_2021_CVPR,
author = {Kim, Chanho and Fuxin, Li and Alotaibi, Mazen and Rehg, James M.},
title = {Discriminative Appearance Modeling With Multi-Track Pooling for Real-Time Multi-Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {9553-9562}
}
```