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Quasi-Dense Similarity Learning for Multiple Object Tracking, CVPR 2021 (Oral)


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Quasi-Dense Tracking



We released a new version of our paper with new benchmark results setting a new SOTA on BDD100K!

QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking

This is the offical implementation of paper Quasi-Dense Similarity Learning for Multiple Object Tracking.

We present a trailer that consists of method illustrations and tracking visualizations. Our project website contains more information:

If you have any questions, please go to Discussions.


Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning. We can naturally combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacement regression or motion priors. We also find that the resulting distinctive feature space admits a simple nearest neighbor search at the inference time. Despite its simplicity, QDTrack outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks. It achieves 68.7 MOTA at 20.3 FPS on MOT17 without using external training data. Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.

Quasi-dense matching

Main results

Without bells and whistles, our method outperforms the states of the art on MOT, BDD100K, Waymo, and TAO benchmarks with ResNet-50 as the base model.

BDD100K test set

35.5 52.3 10790


Dataset MOTA IDF1 ID Sw. MT ML
MOT16 69.8 67.1 1097 316 150
MOT17 68.7 66.3 3378 957 516

Waymo validation set

Category MOTA IDF1 ID Sw.
Vehicle 55.6 66.2 24309
Pedestrian 50.3 58.4 6347
Cyclist 26.2 45.7 56
All 44.0 56.8 30712


Split AP50 AP75 AP
val 16.1 5.0 7.0
test 12.4 4.5 5.2


Please refer to for installation instructions.


Please refer to for dataset preparation and running instructions.

Trained models for testing

More implementations / models on the following benchmarks will be released later

  • MOT16 / MOT17 / MOT20

Waymo models won't be available publicly due to the dataset license constraints.


  title={QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking},
  author={Fischer, Tobias and Pang, Jiangmiao and Huang, Thomas E and Qiu, Linlu and Chen, Haofeng and Darrell, Trevor and Yu, Fisher},
  journal={arXiv preprint arXiv:2210.06984},

  title = {Quasi-Dense Similarity Learning for Multiple Object Tracking},
  author = {Pang, Jiangmiao and Qiu, Linlu and Li, Xia and Chen, Haofeng and Li, Qi and Darrell, Trevor and Yu, Fisher},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  month = {June},
  year = {2021}