mmdetection/configs/bytetrack/README.md

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Abstract

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 score ranging from 1 to 10 points. To put forwards the state-of-the-art performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU.

Citation

@inproceedings{zhang2021bytetrack,
  title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
  author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
  journal={arXiv preprint arXiv:2110.06864},
  year={2021}
}

Results and models on MOT17

Please note that the performance on MOT17-half-val is comparable with the performance reported in the manuscript, while the performance on MOT17-test is lower than the performance reported in the manuscript.

The reason is that ByteTrack tunes customized hyper-parameters (e.g., image resolution and the high threshold of detection score) for each video in MOT17-test set, while we use unified parameters.

Method	Detector	Train Set	Test Set	Public	Inf time (fps)	HOTA	MOTA	IDF1	FP	FN	IDSw.	Config	Download
ByteTrack	YOLOX-X	CrowdHuman + MOT17-half-train	MOT17-half-val	N	-	67.5	78.6	78.5	12852	21060	672	config	model | log
ByteTrack	YOLOX-X	CrowdHuman + MOT17-half-train	MOT17-test	N	-	61.7	78.1	74.8	36705	85032	2049	config	model | log

Results and models on MOT20

Since there are only 4 videos in MOT20-train, ByteTrack is validated on MOT17-train rather than MOT20-half-train.

Please note that the MOTA on MOT20-test is slightly lower than that reported in the manuscript, because we don't tune the threshold for each video.

Method	Detector	Train Set	Test Set	Public	Inf time (fps)	HOTA	MOTA	IDF1	FP	FN	IDSw.	Config	Download
ByteTrack	YOLOX-X	CrowdHuman + MOT20-train	MOT17-train	N	-	57.3	64.9	71.8	33,747	83,385	1,263	config	model | log
ByteTrack	YOLOX-X	CrowdHuman + MOT20-train	MOT20-test	N	-	61.5	77.0	75.4	33,083	84,433	1,345	config	model | log

Get started

1. Development Environment Setup

Tracking Development Environment Setup can refer to this document.

2. Dataset Prepare

Tracking Dataset Prepare can refer to this document.

3. Training

Due to the influence of parameters such as learning rate in default configuration file, we recommend using 8 GPUs for training in order to reproduce accuracy. You can use the following command to start the training.

3.1 Joint training and tracking

Some algorithm like ByteTrack, OCSORT don't need reid model, so we provide joint training and tracking for convenient.

# Training Bytetrack on crowdhuman and mot17-half-train dataset with following command
# The number after config file represents the number of GPUs used. Here we use 8 GPUs
bash tools/dist_train.sh configs/bytetrack/bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py 8

3.2 Separate training and tracking

Of course, we provide train detector independently like SORT, DeepSORT, StrongSORT. Then use this detector to track.

# Training Bytetrack on crowdhuman and mot17-half-train dataset with following command
# The number after config file represents the number of GPUs used. Here we use 8 GPUs
bash tools/dist_train.sh configs/bytetrack/yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py 8

If you want to know about more detailed usage of train.py/dist_train.sh/slurm_train.sh, please refer to this document.

4. Testing and evaluation

4.1 Example on MOTxx-halfval dataset

4.1.1 use joint trained detector to evaluating and testing

bash tools/dist_test_tracking.sh configs/bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py 8 --checkpoint ${CHECKPOINT_FILE}

4.1.2 use separate trained detector to evaluating and testing

bash tools/dist_test_tracking.sh configs/bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py 8 --detector ${CHECKPOINT_FILE}

4.1.3 use video_baesd to evaluating and testing

we also provide two_ways(img_based or video_based) to evaluating and testing. if you want to use video_based to evaluating and testing, you can modify config as follows

val_dataloader = dict(
    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False))

4.2 Example on MOTxx-test dataset

If you want to get the results of the MOT Challenge test set, please use the following command to generate result files that can be used for submission. It will be stored in ./mot_17_test_res, you can modify the saved path in test_evaluator of the config.

bash tools/dist_test_tracking.sh configs/bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17test.py 8 --checkpoint ${CHECKPOINT_FILE}

If you want to know about more detailed usage of test_tracking.py/dist_test_tracking.sh/slurm_test_tracking.sh, please refer to this document.

5.Inference

Use a single GPU to predict a video and save it as a video.

python demo/mot_demo.py demo/demo_mot.mp4 configs/bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py --checkpoint ${CHECKPOINT_FILE} --out mot.mp4

If you want to know about more detailed usage of mot_demo.py, please refer to this document.