Codes for the paper "A closer look at the joint training of object detection and Re-identification in multi-object tracking" (IEEE Transactions on Image Processing, 2022)
Our main contributions, Identity-aware Label Assignment and Discriminative Focal Loss are implemented in ada_matcher.py and ada_loss.py.
The entire project is largely based on FairMOT (FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking), most of the upgraded ccomponments in AdaMOT are named as "ada_xxx.py".
Our results:
(To train) python ada_train.py --exp_id adamot_17 --load_model detector_pretrained_weights.pth --data_cfg src/lib/cfg/mot17.json --batch_size 24 --lr_step 20 --pos 7 --id_aware
(To test) python ada_track.py --load_model trained_weights.pth --conf_thres 0.4 --test_mot17 True
Other usage is identitcal to FairMOT
The pre-trained weights.
The codes and of pre-trained weights of our improved CenterNet detector inside AdaMOT.
