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# Towards-Realtime-MOT
**NEWS:**
- **[2020.01.29]** More models uploaded! The fastest one runs at around **38 FPS!**.
- **[2019.10.11]** Training and evaluation data uploaded! Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for details.
- **[2019.10.01]** Demo code and pre-trained model released!
## Introduction
This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **22~38 FPS** (Note this speed is for the entire system, including the detection step! ) .
We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles.
@ -29,7 +30,7 @@ python demo.py --input-video path/to/your/input/video --weights path/to/model/we
--output-format video --output-root path/to/output/root
```
## docker demo example
## Docker demo example
```bash
docker build -t towards-realtime-mot docker/
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## Dataset zoo
Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for detailed description of the training/evaluation datasets.
## Pretrained model and baseline models
Darknet-53 ImageNet pretrained: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
Darknet-53 ImageNet pretrained model: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
Trained models:
|Model| MOTA | IDF1 | IDS | FP | FN | FPS | Link |
|-----|------|------|-----|----|----|-----|------|
|JDE-1088x608-uncertainty| 74.8| 67.3| 1189| 5558| 21505| 22.2| [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q) |
|JDE-864x480-uncertainty| 70.8| 65.8| 1279| 5653| 25806| 20.3| [[Google Drive]]() [[Baidu NetDisk]]() |
|JDE-576x320-uncertainty| 63.7| 63.3| 1307| 6657| 32794| 37.9|[[Google Drive]]() [[Baidu NetDisk]]() |
The performance is tested on the MOT-16 training set, just for reference. Running speed is tested on an Nvidia Titan Xp GPU. For a more comprehensive comparison with other methods you can test on MOT-16 test set and submit a result to the [MOT-16 benchmark](https://motchallenge.net/results/MOT16/?det=Private). Note that the results should be submitted to the private detector track.
JDE-1088x608-uncertainty: [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q)
## Test on MOT-16 Challenge
```
python track.py --cfg ./cfg/yolov3_1088x608.cfg --weights /path/to/model/weights
```
By default the script runs evaluation on the MOT-16 training set. If you want to evaluate on the test set, please add `--test-mot16` to the command line.
Results are saved in text files in `$DATASET_ROOT/results/*.txt`. You can also add `--save-images` or `--save-videos` flags to obtain the visualized results. Visualized results are saved in `$DATASET_ROOT/outputs/`
## Training instruction
- Download the training datasets.