This code is centerNet[1] version of yolov + deepsort[2], which implemented on CUDA 9.0, ubuntu 16.04, and Anaconda python 3.6.

conda env create -f CenterNet.yml
pip install -r requirments.txt

1. Change CENTERNET_ROOT to your local directory in demo_centernet_deepsort.py.

CENTERNET_PATH = 'CENTERNET_ROOT/CenterNet/src/lib/'

to

e.g) CENTERNET_PATH = '/home/kyy/centerNet-deep-sort/CenterNet/src/lib/'

2. Run demo

Using sample video, we can track multi person.

python demo_centernet_deepsort.py

In test step, we used 'ctdet_coco_dla_2x.pth' model in centernet model zoo.
Change two lines if want to use another model(e.g resdcn18.pth).

#MODEL_PATH = './CenterNet/models/ctdet_coco_dla_2x.pth'
#ARCH = 'dla_34'

to

MODEL_PATH = './CenterNet/models/ctdet_coco_resdcn18.pth'
ARCH = 'resdcn_18'

GPU : one 1080ti 11G

(Left) CenterNet based tracker: fps 18-23 / (Rright) original yolov3 version[2] : fps 8-9

Additionally, fps 30~35 for ctdet_coco_resdcn18 model

coco API provides the mAP evaluation code on coco dataset. So we changed that code slightly to evaluate AP for person class (line 458-464 in 'cocoapi/PythonAPI/pycocotools/cocoeval.py' same as 'tools/cocoeval.py').

The result is like below.

dataset : coco 2017 Val images.
model : ctdet_coco_resdcn18 model

category : 0 : 0.410733757610904 #person AP
category : 1 : 0.20226150054237374 #bird AP
....
category : 79 : 0.04993736566987926
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280 #original

[1] https://github.com/xingyizhou/CenterNet
[2] https://github.com/ZQPei/deep_sort_pytorch