Commands

Extra Installation pip install grad-cam ttach

Detection

CUDA_VISIBLE_DEVICES=2 python tools/test.py configs/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k.py \
/ssd1/chenwy/dataset/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k-13328aed.pth \
--eval bbox --show

Tracking

CUDA_VISIBLE_DEVICES=2 python tools/test.py configs/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k.py \
/ssd1/chenwy/dataset/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k-13328aed.pth \
--eval track --show

CUDA_VISIBLE_DEVICES=3 python tools/test.py configs/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k.py /ssd1/chenwy/dataset/bdd100k/qdtrack-frcnn_r50_fpn_12e_bdd100k-13328aed.pth  --eval track --prune 0.7

Quasi-Dense 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: vis.xyz/pub/qdtrack.

If you have any questions, please go to Discussions.

Abstract

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

mMOTA	mIDF1	ID Sw.
35.5	52.3	10790

MOT

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

TAO

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

Installation

Please refer to INSTALL.md for installation instructions.

Usages

Please refer to GET_STARTED.md for dataset preparation and running instructions.

Trained models for testing

• BDD100K model
• TAO model

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.

Citation

@InProceedings{qdtrack,
  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}
}