Real time one-stage multi-class & multi-object tracking based on anchor-free detection and ReID
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 is a public dataset for 4 CV challenges: object detection, crowd counting, single class multi-object tracking, multi-class multi-object tracking.
from gen</em>dataset_visdrone import cls2id, id2cls # visdrone</p>
* 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.addargument('--reidcls_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 idweight to 1 for tracking and 0 for detection mode. ``` self.parser.addargument('--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
You can also refer to the ropo：MCMOT_YOLOV4 This is MCMOT with CenterNet detection frame work replaced with an anchor-based detection framework.