单阶段实时多类别多目标跟踪
This is an extention work of FairMOT, which extends the one-class multi-object tracking to multi-class multi-object tracking
You can refer to origin fork FairMOT
VisDrone link
VisDrone is a public dataset for 4 CV challenges: object detection, crowd counting, single class multi-object tracking, multi-class multi-object tracking.
- Download multi-class multi-object tracking part of Visdrone dataset
- Using gen_dataset_visdrone.py script to generate labels.
- Call the gen_dot_train_file function in gen_dataset_visdrone.py to generate the dot train file for VisDrone mcmot training task.
- Uncomment cls2id and id2cls in multitracker.py to use the correct class names and class Ids mapping.
from gen_dataset_visdrone import cls2id, id2cls # visdrone
# from gen_labels_detrac_mcmot import cls2id, id2cls # mcmot_c5
- Set cls ids for visdrone training n opts.py i.e.
1~10 object classes are what we need
non-interest-zone (0)
pedestrian (1) --> 0
people (2) --> 1
bicycle (3) --> 2
car (4) --> 3
van (5) --> 4
truck (6) --> 5
tricycle (7) --> 6
awning-tricycle (8) --> 7
bus (9) --> 8
motor (10) --> 9
others (11)
self.parser.add_argument('--reid_cls_ids',
default='0,1,2,3,4,5,6,7,8,9', # '0,1,2,3,4' or '0,1,2,3,4,5,6,7,8,9'
help='') # the object classes need to do reid
Set id_weight to 1 for tracking and 0 for detection mode.
self.parser.add_argument('--id_weight',
type=float,
default=1, # 0for detection only and 1 for detection and re-ida
help='loss weight for id') # ReID feature extraction or not
HRNet18 backbone with bi-linear upsampling replaced with de-convolution
The pre-trained model is for 5 classes(C5) detection & tracking: car, bicycle, person, cyclist, tricycle, which can be used for road traffic video surveillance and analysis.
baidu drive link extract code：ej4p
one drive link
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