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 ECCV 2020 paper. By using this repo, you can simply achieve MOTA 64%+ on the "private" protocol of MOT-16 challenge, and with a near real-time speed at 22~38 FPS (Note this speed is for the entire system, including the detection step! ).
Faster object tracking pipeline for real time tracking (https://arxiv.org/abs/2011.03910v1) is introduced in this project repository.
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.
- Python 3.6
- Pytorch >= 1.2.0
- python-opencv
- py-motmetrics (
pip install motmetrics) - cython-bbox (
pip install cython_bbox) - (Optional) ffmpeg (used in the video demo)
- (Optional) syncbn (compile and place it under utils/syncbn, or simply replace with nn.BatchNorm here)
- Apex
Usage:
Usage:
python demo.py --input-video path/to/your/input/video --weights path/to/model/weights --output-format video --output-root path/to/output/root --batch-size n
## Pretrained model and baseline models
Darknet-53 ImageNet pretrained model: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
## 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/`