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A simple, fast, and strong multi-object tracker that associates every detection box.

ByteTrack is a multi-object tracking (MOT) method that associates every detection box, including those with low scores, to recover true objects and filter out background detections. This approach addresses the problem of missing objects and fragmented trajectories caused by discarding low-score detection boxes in traditional MOT methods. ByteTrack demonstrates significant improvements in IDF1 scores when applied to various state-of-the-art trackers and achieves high MOTA, IDF1, and HOTA scores on the MOT17 test set. Implemented using YOLOX for detection, it provides demo links for Google Colab and Huggingface Spaces. The tracker can be installed and used on a host machine or via Docker, with detailed instructions provided for data preparation and model training, supporting datasets like MOT17, MOT20, CrowdHuman, Cityperson, and ETHZ.
ByteTrack is a multi-object tracking (MOT) method that associates every detection box, including those with low scores, to recover true objects and filter out background detections.
Explore all tools that specialize in trajectory analysis. This domain focus ensures ByteTrack delivers optimized results for this specific requirement.
Associates low score detection boxes with tracklets to recover true objects and filter out background detections, improving tracking accuracy.
Uses YOLOX as the object detector, providing a strong baseline for detection performance.
Supports multiple datasets, including MOT17, MOT20, CrowdHuman, Cityperson, and ETHZ, allowing for versatile training and evaluation.
Provides a Dockerfile for easy deployment and reproducibility.
Offers a model zoo with pre-trained models for different datasets and configurations.
Supports FP16 training, reducing memory usage and accelerating the training process.
Clone the ByteTrack repository using `git clone https://github.com/ifzhang/ByteTrack.git`.
Navigate to the ByteTrack directory using `cd ByteTrack`.
Install the required dependencies using `pip3 install -r requirements.txt`.
Install pycocotools using `pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'`.
Install cython_bbox using `pip3 install cython_bbox`.
Prepare the datasets (MOT17, MOT20, etc.) under the `<ByteTrack_HOME>/datasets` directory.
Convert the datasets to COCO format using the provided scripts (e.g., `python3 tools/convert_mot17_to_coco.py`).
Train the model using the provided training scripts (e.g., `python3 tools/train.py -f exps/example/mot/yolox_x_ablation.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth`).
Track objects using the `track.py` script.
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