SLATracker
This repository hosts our code for our paper Spatial-Attention Location-Aware Multi-Object Tracking. (arxiv link not available now)
The code will be released after the paper published.
Demo Bilibili
Requirements
- Python3.6
- Pytorch 1.6.0, torchvision 0.7.0
- detectron2
- python-opencv
- py-motmetrics
- cython-bbox
Installation
See INSTALL.md
Quick Start
Dataset Zoo
See DATASET_ZOO.md
Note that we transform all used datasets to COCO format for training convenience.
The pre-processing code will be uploaded later. Or you can download our transformed .json files from GoogleDriveTraining
python3 train_net.py --config-file configs/faster_rcnn_R_50_FPN_1x.yamlVisualization
A trained model is available at GoogleDrive or BaiduDisk (kw:sucy)
python3 demo/vis_track.py --config-file configs/faster_rcnn_R_50_FPN_1x.yaml --opts MODEL.WEIGHTS output/model_final.pthMOTChallenge Results
Public
Benchmark MOTA IDF1 HOTA MOTP MT ML FP FN IDSw 2DMOT15 47.0 57.9 43.0 75.3 22.6 27.2 9044 22986 558 MOT16 60.6 59.5 46.8 78.0 24.2 29.1 5783 65469 643 MOT17 59.7 63.4 49.1 77.7 24.0 31.1 16644 209318 1647
Note that we utilize the pre-processing method of Tracktor, not CenterTrack.
Private
| Benchmark | MOTA | IDF1 | HOTA | MOTP | MT | ML | FP | FN | IDSw |
|---|---|---|---|---|---|---|---|---|---|
| 2DMOT15 | 57.9 | 62.2 | 47.1 | 75.8 | 39.1 | 14.7 | 6973 | 18313 | 577 |
| MOT16 | 72.0 | 69.6 | 54.7 | 77.9 | 37.3 | 20.9 | 7242 | 43147 | 740 |
| MOT17 | 71.8 | 69.0 | 54.4 | 77.8 | 38.0 | 20.5 | 19077 | 137700 | 2493 |
Acknowledgement
A large part of the code is borrowed from Zhongdao/Towards-Realtime-MOT and DeanChan/HOIM-PyTorch. Thanks for their wonderful works.