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Quasi-Dense Similarity Learning for Multiple Object Tracking

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 directly combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacementregression 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, QD-Track 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.

Citation

@inproceedings{pang2021quasi,
  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={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={164--173},
  year={2021}
}

Results and models on MOT17

Method Detector Train Set Test Set Public Inf time (fps) HOTA MOTA IDF1 FP FN IDSw. Config Download
QDTrack Faster R-CNN half-train half-val N - 57.1 68.2 68.5 8373 42939 1071 config model | log
QDTrack Faster R-CNN CrowdHuman + half-train half-val N - 59.1 71.7 71.6 6072 38733 867 config model | log

Results and models on LVIS dataset

Method Detector Train Set Test Set Inf time (fps) AP AP50 AP75 AP_S AP_M AP_L Config Download
QDTrack Faster R-CNN LVISv0.5+COCO2017 TAO validation - 17.2 28.6 17.7 5.3 13.0 22.1 config model | log

Results and models on TAO dataset

Note: If you want to achieve a track AP of 11.0 on the TAO dataset, you need to do pre-training on LVIS dataset.

a. Pre-train the QDTrack on LVISv0.5+COCO2017 training set.

The pre-trained checkpoint is given above(model).

b. Save the model to ckpts/tao/**.pth, and modify the configs for TAO accordingly(set load_from to your ckpt path).

We observe around 0.5 track AP fluctuations in performance, and provide the best model.

Method Detector Train Set Test Set Inf time (fps) Track AP(50:75) Track AP50 Track AP75 Config Download
QDTrack Faster R-CNN TAO train TAO validation - 11.0 15.8 6.1 config model | log