MCMOT: One-shot multi-class multi-object tracking </br>

单阶段实时多类别多目标跟踪 </br> This is an extention work of FairMOT, which extends the one-class multi-object tracking to multi-class multi-object tracking </br> 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 </br> 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

Tracking or detection mode setting

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

Pretained model for C5 and VisDrone detection and tracking

HRNet18 backbone with bi-linear upsampling replaced with de-convolution </br> 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. </br> </br> baidu drive link extract code：ej4p </br> 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 </br> This is MCMOT with CenterNet detection frame work replaced with an anchor-based detection framework.

Using ByteTrack

You can also refer to the ropo：MCMOT-ByteTrack </br> Using YOLOX as front-end and using ByteTrack as back-end.