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CaptainEven
169 Stars 35 Forks MIT License 244 Commits 24 Opened issues

Description

Real time one-stage multi-class & multi-object tracking based on anchor-free detection and re-id

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MCMOT: One-shot multi-class multi-object tracking

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

Tracking demo of C5(car, bicycle, person, cyclist, tricycle)

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Tracking demo of VISDrone dataset

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VisDrone dataset training with 10 object classes

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 gendatasetvisdrone.py script to generate labels.
  • Call the gendottrainfile function in gendatasetvisdrone.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</em>dataset_visdrone import cls2id, id2cls  # visdrone</p>
    

from genlabelsdetracmcmot import cls2id, id2cls # mcmotc5

* 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 ```

Tracking or detection mode setting

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 ```

Pretained model for C5 and VisDrone detection and tracking

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

Resnet18 backbone for C5,which is much smaller than HRNet18

ResNet18 one drive link

Resnet18 backbone for VisDrone mcmot

Resnet18 one drive link

Using YOLOV4 as detector

You can also refer to the ropo:MCMOT_YOLOV4 This is MCMOT with CenterNet detection frame work replaced with an anchor-based detection framework.

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