StrongSORT

StrongSORT: Make DeepSORT Great Again

StrongSORT: Make DeepSORT Great Again

Yunhao Du, Yang Song, Bo Yang, Yanyun Zhao

arxiv 2202.13514

Abstract

Existing Multi-Object Tracking (MOT) methods can be roughly classified as tracking-by-detection and joint-detection-association paradigms. Although the latter has elicited more attention and demonstrates comparable performance relative to the former, we claim that the tracking-by-detection paradigm is still the optimal solution in terms of tracking accuracy. In this paper, we revisit the classic tracker DeepSORT and upgrade it from various aspects, i.e., detection, embedding and association. The resulting tracker, called StrongSORT, sets new HOTA and IDF1 records on MOT17 and MOT20. We also present two lightweight and plug-and-play algorithms to further refine the tracking results. Firstly, an appearance-free link model (AFLink) is proposed to associate short tracklets into complete trajectories. To the best of our knowledge, this is the first global link model without appearance information. Secondly, we propose Gaussian-smoothed interpolation (GSI) to compensate for missing detections. Instead of ignoring motion information like linear interpolation, GSI is based on the Gaussian process regression algorithm and can achieve more accurate localizations. Moreover, AFLink and GSI can be plugged into various trackers with a negligible extra computational cost (591.9 and 140.9 Hz, respectively, on MOT17). By integrating StrongSORT with the two algorithms, the final tracker StrongSORT++ ranks first on MOT17 and MOT20 in terms of HOTA and IDF1 metrics and surpasses the second-place one by 1.3 - 2.2. Code will be released soon.

vs. SOTA

Data&Model Preparation

1. Download MOT17 & MOT20 from the official website.

   path_to_dataset/MOTChallenge
   ├── MOT17
   	│   ├── test
   	│   └── train
   └── MOT20
       ├── test
       └── train
   

2. Download our prepared data

   path_to_dataspace
   ├── AFLink_epoch20.pth  # checkpoints for AFLink model
   ├── MOT17_ECC_test.json  # CMC model
   ├── MOT17_ECC_val.json  # CMC model
   ├── MOT17_test_YOLOX+BoT  # detections + features
   ├── MOT17_test_YOLOX+simpleCNN  # detections + features
   ├── MOT17_trainval_GT_for_AFLink  # GT to train and eval AFLink model
   ├── MOT17_val_GT_for_TrackEval  # GT to eval the tracking results.
   ├── MOT17_val_YOLOX+BoT  # detections + features
   ├── MOT17_val_YOLOX+simpleCNN  # detections + features
   ├── MOT20_ECC_test.json  # CMC model
   ├── MOT20_test_YOLOX+BoT  # detections + features
   ├── MOT20_test_YOLOX+simpleCNN  # detections + features
   

3. Set the paths of your dataset and other files in "opts.py", i.e., root_dataset, path_AFLink, dir_save, dir_dets, path_ECC.

Requirements

• Python3.6
• torch 1.7.0 + torchvision 0.8.0

Tracking

• Run DeepSORT on MOT17-val

  python strong_sort.py MOT17 val

• Run StrongSORT on MOT17-val

  python strong_sort.py MOT17 val --BoT --ECC --NSA --EMA --MC --woC

• Run StrongSORT++ on MOT17-val

  python strong_sort.py MOT17 val --BoT --ECC --NSA --EMA --MC --woC --AFLink --GSI

• Run StrongSORT++ on MOT17-test

  python strong_sort.py MOT17 test --BoT --ECC --NSA --EMA --MC --woC --AFLink --GSI

• Run StrongSORT++ on MOT20-test

  python strong_sort.py MOT20 val --BoT --ECC --NSA --EMA --MC --woC --AFLink --GSI

Note

• To evaluate the tracking results, we recommend using the official code.
• You can also try to apply AFLink and GSI to other trackers.
• Tuning the hyperparameters carefully would brings better performance.

Citation

@misc{2202.13514,
Author = {Yunhao Du and Yang Song and Bo Yang and Yanyun Zhao},
Title = {StrongSORT: Make DeepSORT Great Again},
Year = {2022},
Eprint = {arXiv:2202.13514},
}

Acknowledgement

A large part of the codes, ideas and results are borrowed from DeepSORT, JDE, YOLOX and ByteTrack. Thanks for their excellent work!