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+# OC_SORT
+
+## Model description
+Observation-Centric SORT (OC-SORT) is a pure motion-model-based multi-object tracker. It aims to improve tracking robustness in crowded scenes and when objects are in non-linear motion. It is designed by recognizing and fixing limitations in Kalman filter and SORT. It is flexible to integrate with different detectors and matching modules, such as appearance similarity. It remains, Simple, Online and Real-time.
+
+## Step 1: Installing
+```bash
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
+```
+
+```bash
+cd PaddleDetection
+yum install mesa-libGL -y
+
+pip3 install -r requirements.txt
+pip3 install protobuf==3.20.1
+pip3 install urllib3==1.26.6
+pip3 install scikit-learn
+```
+
+## Step 2: Prepare Datasets
+
+- MOT17_ch datasets
+```bash
+cd dataset/mot
+git clone https://github.com/ifzhang/ByteTrack.git
+```
+
+Download [MOT17](https://motchallenge.net/), [MOT20](https://motchallenge.net/), [CrowdHuman](https://www.crowdhuman.org/), [Cityperson](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md), [ETHZ](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) and put them under <ByteTrack_HOME>/datasets in the following structure:
+
+```bash
+datasets
+   |——————mot
+   |        └——————train
+   |        └——————test
+   └——————crowdhuman
+             └——————CrowdHuman_train
+             └——————CrowdHuman_val
+             └——————annotation_train.odgt
+             └——————annotation_val.odgt
+
+```
+Then, you need to turn the datasets to COCO format and mix different training data:
+```bash
+cd <ByteTrack_HOME>
+python3 tools/convert_mot17_to_coco.py
+python3 tools/convert_crowdhuman_to_coco.py
+```
+
+Mixing different datasets:
+```bash
+cd datasets
+mkdir -p mix_mot_ch/annotations
+cp mot/annotations/val_half.json mix_mot_ch/annotations/val_half.json
+cp mot/annotations/test.json mix_mot_ch/annotations/test.json
+cd mix_mot_ch
+ln -s ../mot/train mot_train
+ln -s ../crowdhuman/CrowdHuman_train crowdhuman_train
+ln -s ../crowdhuman/CrowdHuman_val crowdhuman_val
+
+cd <ByteTrack_HOME>
+python3 tools/mix_data_ablation.py
+```
+Create a data link:
+```bash
+cd PaddleDetection/dataset/mot
+ln -s ByteTrack/datasets/mix_mot_ch mix_mot_ch
+```
+
+- MOT-17 half train datasets
+Download [MOT17](https://bj.bcebos.com/v1/paddledet/data/mot/MOT17.zip) and put them under <PaddleDetection_HOME>/datasets/mot/ in the following structure:
+
+```bash
+MOT17/
+└──images
+   ├── test
+   │   ├── MOT17-01-DPM
+   │   │   ├── det
+   │   │   └── img1
+   │   ├── ...
+   │   └── MOT17-14-SDP
+   │       ├── det
+   │       └── img1
+   └── train
+       ├── MOT17-02-DPM
+       │   ├── det
+       │   ├── gt
+       │   └── img1
+       ├── ...
+       └── MOT17-13-SDP
+           ├── det
+           ├── gt
+           └── img1
+```
+Turn the datasets to COCO format:
+```bash
+cd <ByteTrack_HOME>
+python3 tools/convert_mot17_to_coco.py
+```
+
+Create a data link:
+```bash
+cd <PaddleDetection_HOME>/datasets/mot/MOT17
+ln -s ../ByteTrack/datasets/mot/annotations ./
+```
+
+## Step 3: Training
+
+```bash
+cd PaddleDetection
+
+# mix_mot_ch datasets
+python3 -m paddle.distributed.launch --log_dir=ppyoloe --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/bytetrack/detector/yolox_x_24e_800x1440_mix_mot_ch.yml --eval --amp
+
+# MOT-17 half train datasets
+python3 -m paddle.distributed.launch --log_dir=ppyoloe --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/bytetrack/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml --eval --amp
+
+```
+
+## Step 4: result
+
+| GPUs        | DATASET   | IPS       | MOTA          |
+|-------------|-----------|-----------|--------------|
+| BI-V100 x 8 | MOT-17 half train|  6.5907     | 57.5        |
+## Reference
+- [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)